Please i need with my assignment.To complete the Intervention Development

Get college assignment help at Smashing Essays Question Please i need with my assignment.To complete the Intervention Development Article Review Worksheet, use the selected the article provided below, analyze the use of behavioral health theories, and complete the Intervention Development Article Review Worksheet based on the information you obtained from the article. The article to be : Tittle “Li, X., Zhang, L., Mao, R., Zhao, Q., Stanton, B. (2011). Effect of social cognitive theory-based HIV education prevention program among high school students in Nanjing, China. Health Education Research, 26 (3), 419-431″. The full article can be found in google.Thank you so much.Please below is the intervention review article worksheet and the article that need to be review.Intervention Development Article Review Worksheet2. Identify the health behavior theory used in this article.3. Identify the level of intervention targeted by the intervention (individual, interpersonal, community). 4. Identify the intervention strategy used in this article and provide your assessment of its appropriateness .  5. Summarize how the theory was used in the development of the intervention activities.  6. Provide your assessment of why this theory would or would not be a good fit for Health Promotion Program Proposal. Effect of social cognitive theory-based HIV education prevention program among high school students in Nanjing, China Xiaoming Li1 *, Liying Zhang1 , Rong Mao2 , Qun Zhao1 and Bonita Stanton1 1 Carman and Ann Adams Department of Pediatrics, Prevention Research Center, Wayne State University School of Medicine, 4707 Saint Antoine Street, Hutzel Building, Suite W534, Detroit, MI 48201, USA and 2 Nanjing University Institute of Mental Health, 22 Hankou Road, Nanjing, Jiangsu 210093, China *Correspondence to: X. Li. E-mail: x..i@med.wayne.edu Received on January 25, 2010; accepted on January 9, 2011 Abstract This study was designed to evaluate potential preventive effects of a cultural adaption of the Focus on Kids (FOK) program among Chinese adolescents through a quasi-experimental intervention trial in Nanjing, China. High school students were assigned to either experimental groups (n 5 140) or control groups (n 5 164) by schools (with three schools in each condition). The participants completed a confidential questionnaire at baseline and 6-month postintervention with a follow-up rate of 94.4% (287 of 304). The outcome measures included HIV knowledge, HIV-related perceptions based on the protection motivation theory, stigmatizing attitude toward people living with HIV/ AIDS (PLWHA), intentions of health-related risk behaviors and sexual intercourse in the previous 6 months. Results showed a significant intervention effect at 6-month post-intervention in increasing HIV knowledge, decreasing perceptions of response cost associated with abstinence and reducing stigmatizing attitudes toward PLWHA, after controlling for key demographic characteristics and relevant baseline measures. Further mediation analysis suggested that HIV knowledge mediated the effect of intervention on stigma reduction. Findings from this study support the feasibility and initial efficacy of the cultural adaptation of FOK HIV prevention program among high school students in China. Introduction Adolescent HIV prevention interventions aimed to delay sex initiation and reduce sexual risk behaviors have been conducted in many developed and developing countries in response to the growing HIV epidemic [1]. Global literature indicates that many theory-based HIV prevention interventions conducted among adolescents in developed countries have been efficacious, resulting in substantial reduction of sexual risk behaviors [2-5]. A recent study suggested that about two-thirds of HIV intervention programs implemented among youth in schools, clinics and community settings in the United States showed a positive impact on delaying the debut of sex, reducing the number of sexual partners and increasing contraception use [6]. Another literature review by Speizer et al. [7] suggested that some school-based HIV educational programs in developing countries had effects on behavioral changes. Many school-based HIV interventions in developing countries have demonstrated significant effects on knowledge and attitudes. For example, an intervention implemented among high schools students in KwaZulu Natal in South Africa demonstrated significant improvements in HIV-related HEALTH EDUCATION RESEARCH Vol.26 no.3 2011 Pages 419-431 Advance Access publication 17 February 2011 The Author 2011. Published by Oxford University Press. All rights reserved. For permissions, please email: j..s@oup.com doi:10.1093/her/cyr001 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 knowledge and attitudes [8]. Another intervention implemented among 845 students in four high schools in the semi-urban district of Metro Manila, the Philippines, demonstrated a significant effect in increasing HIV-related knowledge and in improving attitudes toward people living with HIV/AIDS (PLWHA) [9]. However, some school-based interventions failed to demonstrate an effect. For instance, a study conducted in Uganda among 20 intervention schools (n = 1274) and 11 control schools (n = 803) showed no post-intervention improvement in seven of nine areas of HIV-related knowledge, attitudes and intention [10]. HIV knowledge has been a focus of HIV prevention among adolescents. Although it may not be sufficient, HIV knowledge has been proven to be necessary to increase protective sexual behaviors [6]. In addition, incorrect information about HIV may lead to an increase of stigmatizing attitudes toward PLWHA [11]. Therefore, it is important to improve adolescents’ knowledge of HIV and understand the link between HIV knowledge and HIV-related stigma. HIV is among the most stigmatized conditions [12]. HIV-related stigma impacts the social interaction of PLWHA with their family and other people in society [13]. HIV-related stigma has been documented throughout the world, including China [14- 16]. Prejudicial attitudes toward PLWHA and the stigmatization against PLWHA are persistent in Chinese society [17]. For instance, medical personnel who were providing treatments for AIDS patients were less willing to interact with AIDS patients [18]. A study conducted among 4208 rural-to-urban migrants in Beijing and Nanjing suggested that misconception was positively associated with stigmatizing attitudes toward PLWHA [19]. A qualitative study by Yang et al. [20] suggested the existence of the institutional forms of stigma in health care settings and the authors emphasized the importance of stigma reduction interventions. However, few studies have examined the effect of HIV prevention intervention on stigma reduction in the Chinese setting. One HIV stigma reduction intervention study among health care providers conducted in four hospitals in Yunan province suggested that at 3- and 6-month post-intervention, health care providers were less likely to report negative feelings toward PLWHA [21]. Another study conducted in Hong Kong among 600 9th and 10th graders indicated that negative perceptions about PLWHA can be reduced substantially through the school-based stigma reduction intervention [17]. Adolescents in China appear to have limited knowledge about HIV and limited formal sources for HIV-related knowledge and information [22, 23]. In a recent study, while adolescents were aware of the main routes through which HIV is transmitted, they had numerous misconceptions about whether HIV can be transmitted through some daily contacts [24]. A substantial proportion of participants (over 40%) believed that some routine daily activities such as ‘eating food at a restaurant where the cook has HIV/AIDS’, ‘sharing plates, forks or glasses with someone who has HIV/AIDS’, ‘using public toilets’ and ‘coughing or sneezing’ could transmit HIV. Such misconceptions may cause undue fears of HIV contagion, which in turn results in stigmatizing attitudes toward PLWHA. The existing HIV prevention literature has also documented the importance of a theoretical framework in guiding the development of an HIV prevention program [25]. One of such theoretical framework is the protection motivation theory (PMT). PMT is a social cognitive theory, which emphasizes the cognitive processes of behavioral change [26]. PMT describes adaptive and maladaptive responses to a health threat that are the result of two appraisal processes—the threat appraisal process and the coping appraisal process (Fig. 1). The threat appraisal process is the process through which people assess a threat based on environmental and personal factors combining to pose a potential threat. This process includes four factors: intrinsic and extrinsic rewards, which increase the probability of selecting a maladaptive response, and severity and vulnerability, which decrease the probability of selecting a maladaptive response [27]. The coping appraisal process is the process of coping options based on a person’s perceived efficacy and cost of the social and personal factors that contribute to their ability to avert the threat. The X. Li et al. 420 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 coping appraisal includes three factors: self-efficacy which is the perceived ability to carry out the adaptive response, response efficacy which is the belief of the adaptive response being effective and response cost which is associated with taking the adaptive coping response. Response efficacy and self-efficacy increase the probability of selecting an adaptive response, while the response costs decrease the probability of selecting an adaptive response. The two appraisal processes combine to form protection motivation. The interaction of appraisals for the threat and coping responses results in the intention to perform an adaptive response to avert the threat, which promote or mediate behavior changes. Guided by the PMT, the ‘Focus on Kids’ (FOK) was designed as a comprehensive HIV/STI education and prevention program for adolescents and young adults [28]. Supported by the National Institutes of Health, the FOK program was developed, implemented and evaluated among urban African American adolescents in Baltimore in the 1990s [5]. In 1998, Centers for Disease Control and Prevention designated FOK as one of the ‘Programs that Work’ nationwide in the United States. FOK has been identified as an efficacious evidence-based intervention program for HIV prevention and has been disseminated both national and internationally [29]. The FOK program has been demonstrated to reduce adolescent risk behaviors and/or perceptions in many non-Western cultural settings, including Namibia [30], Vietnam [31], China [32] and the Bahamas [33]. A study conducted in 1997 among Namibian adolescents indicated that the culturally adapted FOK intervention program had an effect on condom use at 2-month post-intervention and had an effect on abstinence at 12-month post-intervention [30]. The study conducted in Vietnam in 2001 showed a significant intervention effect on the seven PMT constructs at immediate post-intervention and at 6- month post-intervention [31]. The Vietnam study also showed effect on intention of future-condomuse but not intention of abstinence. Several studies in Asian countries did not find significant intervention effect on sexual behaviors because of the relatively low rates of sexual activities among adolescents in those countries [31, 32]. The culturally adapted FOK program demonstrated a significant effect on delaying behavioral progression toward risky sex among children and adolescents in the Bahamas [33]. A number of HIV prevention programs have been carried out in China in recent years [34, 35]. However, these efforts mainly targeted high risk or adult populations, including intravenous drug users Fig. 1. Theoretical framework of PMT. HIV prevention intervention among adolescents in China 421 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 [36], female sex workers [37-39], men who have sex with men [40] and college students [32]. Some school-based HIV prevention intervention studies demonstrated some positive effects on knowledge, attitudes and behaviors. However, they were either with a short follow-up period (e.g. 3 months) or with a low follow-up rate [41, 42]. Moreover, there have been limited efforts to design and implement theoretically driven school-based HIV prevention interventions targeting adolescents in China. To promote HIV prevention intervention among adolescents and youth in China, we have culturally adapted FOK for college students and high school students in China since 2001. The findings from the college students intervention study showed that at 6-month post-intervention, HIV knowledge and protective perceptions were increased while the intention to have sex in the next 6 months was decreased among youth in the intervention condition [32]. However, data from high school students have not been reported to date. The purpose of the current study was to examine the intervention effects on HIV-related knowledge, perceptions, attitudes and intentions among high school students. The findings may inform the development of effective school-based HIV education prevention programs in China and other developing countries. Methods Study site and participants Nanjing, the capital city of Jiangsu province in eastern China, was the site for the current study. Nanjing, with a population over 6 million, is the second largest commercial center after Shanghai in the east region of China. Nanjing has also served as a national hub of education, research, transportation and tourism throughout Chinese history. Six high schools in the Nanjing metropolitan area participated in this pilot intervention program in which three schools were randomly assigned to the intervention condition. In these three schools, 140 students (one classroom per school) voluntarily received the culturally adapted FOK prevention curriculum. The other three schools were assigned to the control condition in which 164 students (one classroom per school) voluntarily participated in the assessment. The follow-up rate at 6-month post-intervention was 94.4% (287 of 304). The research protocol was approved by the Institutional Review Boards of West Virginia University in the United States and Nanjing University in China. Cultural adaptation of the FOK curriculum The intervention curriculum in this study was a cultural adaptation of the FOK HIV prevention program [5]. The FOK curriculum and its assessment tool—the Youth Health Risk Behavior Inventory [43] were adapted into Chinese settings based on extensive qualitative and quantitative data collected from high school students and college students [32, 44]. The culturally adapted curriculum consists of eight sessions with a total delivery time of approximately 12 hours (90 min for each session). Each of the sessions explored one or more of the seven PMT constructs (i.e. extrinsic rewards, intrinsic rewards, severity, vulnerability, response efficacy, self-efficacy and response cost). The process and context of the cultural adaption of the curriculum into Chinese setting have been described elsewhere [32, 44]. In addition to the changes made to the curriculum and survey instrument for college students [32], further modifications were made in response to the developmental stage of high school students. These additional changes include (i) the change of intervention focus from both abstinence and safe sex for college students to mainly abstinence for high school students as the rate of sexual initiation was low (e.g. 4.8%) among high school students at the time of the study [44], (ii) the removal of explicit materials related to condom use (e.g. condom demonstration and condom race—activities to demonstrate the correct way of using a condom on a penis model) in both curriculum and survey instrument at the request of the local schools and (iii) change of stories, games and fictional figures in the vignettes in the intervention curriculum so that the content/format is developmentally appropriate for the high school students. Although condom use was one of the primary outcomes in the original FOK curriculum, similar adaptation (i.e. removal X. Li et al. 422 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 of condom-related materials from the intervention curriculum) has been done among adolescents in both the United States (e.g. rural West Virginia) [45] and overseas (e.g. Vietnam) [31], where the inclusion of explicit materials or content related to condom use was considered either developmentally or culturally inappropriate. Delivery of the intervention After receiving extensive training from the US and Chinese investigators, four graduate students and seven faculty members from a university in Nanjing delivered the culturally adapted high school student version of FOK curriculum to students in the intervention group. Permission was obtained from the local education bureau and school administrators to conduct research in participating schools. Written informed consent forms were provided by all students before their participation in the study. Consistent with the common practice of involving school students in research in China at the time of the study [22, 44], no parental consent was obtained. Students in the intervention classrooms were divided into several small groups (e.g. about 10 students in each group) for curriculum-related activities (e.g. games, role-play). Assessment procedure Data collection was conducted at baseline (before intervention delivery) and 6-month postintervention by trained interviewers. All participating students (both intervention and control groups) completed a self-administered confidential questionnaire in their classrooms during regular school hours. The participants were assured of the confidentiality of their responses. School personnel were not allowed to present in the classroom during the survey. A total of 304 high school students aged 15-19 years participated in the baseline survey and 287 (94.4%) of the baseline cohort participated in the 6-month post-intervention survey. Measures Demographic characteristics Participants were asked to provide information on their age, gender, school performance, family composition, parental education and family income (in Chinese currency Yuan, 1 US dollar equals 8.27 Chinese Yuan at the time of survey). The school performance in academic classes was measured in letter grade using a 5-point scoring system (5 = mostly As, 4 = mostly Bs, 3 = mostly Cs, 2 = mostly Ds and 1 = mostly high school) in the current study because of the small frequency in categories of ‘5000 Yuan). PMT constructs Seven PMT constructs related to early sexual initiation or abstinence were measured using 24 questions in the survey. Extrinsic rewards. Perception of extrinsic rewards was assessed using eight items (e.g. perceived number of boys and girls who were dating, who had had sex, who had multiple sex partners). The Cronbach’s alpha for this scale is 0.62. These items had a 4-point response option ranging from ‘none’ to ‘most’. Intrinsic rewards. Youth’s perception of intrinsic rewards was assessed with one item by asking participants how likely for them to take the risk (having sex) in order to ‘be the same’ as some other classmates. This item had a 4-point response option (unlikely, somewhat likely, likely and very likely). Severity. Perceived severity or negative consequences of early sexual initiation and sexual risk behavior were assessed using five items (a = 0.61) (e.g. ‘It is possible to get HIV in the first sex’, ‘It is possible to get (someone) pregnant in the first sex’, ‘One may get HIV through a single sexual intercourse’ and ‘One may get HIV if he/she HIV prevention intervention among adolescents in China 423 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 has multiple sexual partners’). Each of these statements had a 4-point response option (strongly disagree, disagree, agree and strongly agree). Vulnerability. Perceived vulnerability to early sexual initiation was assessed using two items (a = 0.46) regarding the likelihood of having sex before graduation or before marriage. These items had a 5-point response option (very unlikely, unlikely, uncertain, likely and very likely). Response efficacy. Response efficacy had four items (a = 0.67) assessing perceptions of the effectiveness of abstinence (e.g. ‘It is better for adolescents to have no sex’, ‘No sex at my age is good for health’ and ‘No sex at my age is good for study’). These items had a 4-point response option (strongly disagree, disagree, agree and strongly agree). Self-efficacy. Self-efficacy was measured using two questions (‘Even though many of my friends have sex, I will not have sex at a young age’ and ‘If my boy/girlfriend wants to have sex, I will not do it’). The two items had a 4-point response option (strongly disagree, disagree, agree and strongly agree). This scale had Cronbach’s alpha of 0.68. Response cost. This construct was assessed using two items (a = 0.47) regarding the possible barriers or negative consequences of abstinence (e.g. not having sex make relationship unstable, not having sex is not good for love relationship). These two items had a 4-point response option (strongly disagree, disagree, agree and strongly agree). HIV knowledge Participants were asked to give answers to a list of 13 questions related to transmission/nontransmission routes (e.g. ‘Share plates, fork or glasses with someone who has HIV’, ‘Share needles for intravenous drug use with some HIVinfected drug users”). These 13 items initially had a 4-point response option (very likely, likely, unlikely and very unlikely) which were dichotomized into ‘0 = incorrect’ and ‘1 = correct’ for the purpose of data analysis. The sum score was used as a composite score ranging from 0 to 13 with a higher score indicating a higher level of HIV knowledge. These 13 items had an adequate reliability estimate (Cronbach’s alpha = 0.79). Stigmatizing attitude Stigmatizing attitude toward PLWHA was measured using one question (‘If I know someone who has HIV, I will stay far away from him/her’). Participants were asked whether they agreed with the statement or not. This item had a 4-point response option (i.e. strongly disagree, disagree, agree and strongly agree). Intentions to engage in sexual behavior and other health risk behaviors Participants were asked about their intentions to engage in sexual intercourse and some other health risk behaviors (i.e. smoking, alcohol consumption and visiting pornographic website) in the next 6 months. Each intention was assessed with a 4-point response option ranging from ‘very unlikely’ to ‘very likely’. Sexual behavior Participants were asked a dichotomous question: ‘Did you have sexual intercourse in the past 6 months’ (yes/no). Statistical analysis Analysis of variance (ANOVA) or chi-square test was used to examine the baseline differences in age, gender, school performance, family composition, parental education and family income between the intervention group and the control group. In addition, the differences in primary outcomes of the intervention (e.g. HIV-related knowledge, perception and stigmatizing attitude toward PLWHA) between the intervention group and control group at baseline and 6-month post-intervention were examined using ANOVA. Because the majority of PMT constructs had low reliability estimates (e.g. Cronbach’s alpha <0.70 for all PMT scales), the analysis related to PMT constructs was conducted at item level rather than scale level. Following the guidelines by Baron and Kenny [46], multivariate linear regression analysis was performed to examine the role of HIV knowledge in mediating the effect of the intervention program X. Li et al. 424 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 on stigma reduction (Fig. 2). Three multivariate linear regression models were used to test the effect of mediator (HIV knowledge at 6-month post-intervention, M). In the first model, the independent variable (intervention condition, X) was regressed on the mediation variable (M) with a regression coefficient a. In the second model, the independent variable (intervention condition, X) was regressed on the dependent variable (stigmatizing attitude measured at 6-month post-intervention, Y) with a regression coefficient c. In the third model, the independent variable (X) and the mediation variable (M) were simultaneously regressed on dependent variable (Y) with regression coefficients c# (X on Y) and b (M on Y). In addition, age, gender, school performance, family composition, parental education, family income, baseline HIV knowledge and baseline stigmatizing attitude were controlled in each of the regression models. Results Demographic characteristics The mean age for all participating students was 16.3 years (SD = 0.66). The majority of participants (88.5%) were living with both parents. The intervention group and control group were similar in age, school performance, family composition, parental education and family income at baseline (Table I). There were more male students in the intervention group (54.2%) than in the control group (46.2%), although such difference did not reach statistical significance. Intervention effect Table II shows that there were no differences in baseline HIV knowledge between the intervention group and the control group. There was a significant increase of HIV knowledge scores from baseline to 6-month post-intervention in the intervention group (8.49 versus 9.08, P < 0.01) but not in the control group (8.03 versus 8.01). There were no significant differences between the intervention group and the control group in the seven PMT constructs at 6-month post-intervention except the response cost items, which significantly differed between the intervention group and the control group (P < 0.05 for both items). One extrinsic rewards item (e.g. number of female classmates who have a boyfriend) showed a significant difference with the perception being reduced in the intervention group at the 6-month post-intervention. There were no statistically significant group differences on intention of having sex and other health Fig. 2. Results of the mediation analysis. Note: Numbers are unstandardized coefficients and standard errors are given inside the brackets. *P < 0.05; **P < 0.01. HIV prevention intervention among adolescents in China 425 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 risk behaviors. As shown in Table II, stigmatizing attitude scores were similar at baseline between the intervention group and the control group. The stigmatizing attitude in the intervention group was reduced from 2.62 (SD = 0.86) at baseline to 2.57 (SD = 0.76) at 6-month post-intervention (P < 0.05), while there was no significant change on stigmatizing attitude in the control group (2.77 versus 2.81). There were no statistically significant differences between the intervention group and control group on percentage of participants who reported having sexual intercourse at either the baseline (2.5 versus 3.4%) or the 6-month post-intervention (4.8 versus 2.6%). Mediation effect of HIV knowledge Mediation analysis indicated that HIV knowledge mediated the effect of intervention on stigma reduction (Fig. 2). HIV knowledge was positively associated with the intervention condition (regression coefficient = 0.900, P < 0.01) controlling for key demographic factors (age, gender, school performance, family composition, parental education and family monthly income) (Table III). In addition, HIV knowledge was also positively associated with the baseline HIV knowledge (regression coefficient = 0.272, P < 0.01). Model 2 showed a direct effect of intervention on stigma. Stigmatizing attitude was negatively associated with the intervention condition (regression coefficient = 0.186, P < 0.05). Model 3 showed that when both HIV knowledge and intervention condition were included in the model, HIV knowledge was negatively associated with stigmatizing attitude (regression coefficient = 0.052, P < 0.01) while the significant direct effect of intervention on stigmatizing attitude was diminished (regression coefficient was decreased from 0.186, P 0.05). Discussion Findings from this study suggest that the cultural adaption of the FOK intervention program among Table I. Sociodemographic characteristics of study sample Sociodemographic characteristics Overall, n (%) Control group, n (%) Intervention group, n (%) N 287 156 131 Age, mean (SD) 16.33 (0.7) 16.29 (0.6) 16.37 (0.7) School performancea , mean (SD) 3.13 (1.0) 3.19 (1.0) 3.07 (0.9) Gender Male 143 (49.8) 72 (46.2) 71 (54.2) Female 144 (50.2) 84 (53.8) 60 (45.8) Family composition Lived with both parents 254 (88.5) 137 (87.8) 117 (89.3) Other living arrangement 33 (11.5) 19 (12.2) 14 (10.7) Father’s education High school 222 (77.6) 120 (77.4) 102 (77.9) Mother’s education High school 210 (73.7) 114 (73.5) 96 (73.8) Family monthly income (Yuan) 5000 12 (4.3) 4 (2.6) 8 (6.2) a Coding for school performance: 1 = mostly Fs, 2 = mostly Ds, 3 = mostly Cs, 4 = mostly Bs and 5 = mostly As. X. Li et al. 426 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 Table II. Effect of social cognitive theory-based HIV education intervention among high school students in China Outcome measures Pre-intervention 6-month post-intervention Control Intervention Control Intervention N 156 131 156 131 HIV knowledge (mean, SD) 8.03 (3.0) 8.49 (3.0) 8.01 (2.7) 9.08 (2.9)** PMT constructs Extrinsic rewards ‘How many of your male classmates have a girl friend’ 2.95 (0.7) 2.86 (0.7) 3.07 (0.7) 2.92 (0.8) ‘How many of your female classmates have a boy friend’ 2.85 (0.7) 2.80 (0.7) 2.94 (0.8) 2.73 (0.8)* ‘How many of your male classmates have had sex’ 1.47 (0.7) 1.55 (0.8) 1.54 (0.7) 1.66 (0.9) ‘How many of your female classmates have had sex’ 1.37 (0.6) 1.42 (0.7) 1.44 (0.7) 1.55 (0.8) ‘How many of your classmates have visited sex-related internet’ 2.27 (0.9) 2.31 (1.0) 2.36 (0.9) 2.39 (1.0) ‘My friends think that it is very cool to have sex with a boy/girl friend’ 1.71 (1.0) 1.61 (0.9) 1.75 (0.9) 1.66 (1.0) ‘Most students think that it is cool for a male student having multiple sex partner’ 2.04 (0.8) 1.89 (0.7) 1.85 (0.8) 1.82 (0.9) ‘Most students think that it is cool for a female student having multiple sex partner’ 2.73 (0.9) 2.80 (0.9) 1.69 (0.8) 1.77 (0.8) Intrinsic rewards To be same as some other classmates, I likely to take the risk of having sex’ 1.72 (0.8) 1.80 (1.0) 1.93 (0.9) 1.82 (1.0) Severity ‘People obtaining HIV/AIDS usually die soon in a short period after diagnosis’ 2.36 (0.7) 2.21 (0.7) 2.24 (0.7) 2.29 (0.8) ‘It is possible to get HIV in the first sex’ 2.84 (0.7) 2.77 (0.9) 2.79 (0.8) 2.95 (0.8) ‘It is possible to get (someone) pregnant in the first sex’ 2.95 (0.7) 2.78 (0.9) 2.85 (0.8) 2.91 (0.7) ‘One may get HIV through a single sexual intercourse’ 2.95 (0.7) 2.86 (0.9) 2.94 (0.7) 2.98 (0.7) ‘One may get HIV if he/she has multiple sexual partners’ 3.30 (0.6) 3.22 (0.8) 3.29 (0.6) 3.15 (0.7) Vulnerability ‘The likelihood to have sex before graduation’ 3.70 (0.7) 3.62 (0.9) 3.65 (0.8) 3.60 (0.9) ‘The likelihood to have sex before married’ 2.97 (1.1) 2.93 (1.2) 2.79 (1.1) 2.86 (1.2) Response efficacy ‘Having premarital sex can affect my career’ 3.10 (0.9) 2.93 (0.9) 2.92 (0.8) 3.01 (0.9) ‘It is better for adolescents to have no sex’ 3.25 (0.8) 3.23 (0.8) 3.21 (0.7) 3.23 (0.9) ”’No sex at my age is good for health’ 2.80 (0.8) 2.66 (0.9) 2.86 (0.7) 2.78 (0.8) ‘No sex at my age is good for study’ 2.49 (0.9) 2.54 (0.9) 2.63 (0.8) 2.68 (0.9) HIV prevention intervention among adolescents in China 427 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 high school students in China was effective in increasing HIV knowledge and decreasing perceptions of response cost associated with abstinence. In addition, the FOK intervention program had demonstrated an effect in reducing negative attitude toward PLWHA. Data in the current study also showed that HIV knowledge had a mediation effect on reduction of stigma against PLWHA. The intervention appears to reduce stigmatizing attitudes toward PLWHA by increasing the accurate HIV knowledge among adolescents. However, the intervention program has limited effects on PMT constructs. There were several possible reasons for the non-significant changes of most PMT items over time. First, there might be some measurement issues of the PMT constructs. Table II. Continued Outcome measures Pre-intervention 6-month post-intervention Control Intervention Control Intervention Self-efficacy ‘Even though many my friends have sex, I will not have sex in my age’ 3.32 (0.7) 3.33 (0.7) 3.33 (0.8) 3.21 (0.9) ‘If my boy/girl friend requires for having sex, I will refuse to do it’ 3.05 (0.9) 3.11 (0.9) 3.08 (0.9) 3.15 (0.8) Response cost ‘No sex is not good for love relationship’ 2.75 (0.8) 2.59 (0.8) 2.72 (0.8) 2.48 (0.9)* ‘No sex makes relationship instable’ 2.38 (0.8) 2.41 (0.9) 2.49 (0.9) 2.28 (0.9)* Stigmatizing attitude (mean, SD) 2.77 (0.7) 2.62 (0.9) 2.81 (0.8) 2.57 (0.8)* Intentions (mean, SD) Smoke 1.22 (0.7) 1.34 (0.9) 1.38 (0.9) 1.36 (0.9) Consume alcohol 4.98 (2.2) 4.77 (2.1) 5.01 (2.2) 5.04 (2.5) Visit pornographic websites 1.21 (0.6) 1.20 (0.6) 1.21 (0.6) 1.21 (0.7) Engage in sexual intercourse 1.03 (0.3) 1.06 (0.3) 1.06 (0.4) 1.11 (0.5) Sexual intercourse in the last 6 months (%) 3.4 2.5 2.6 4.8 * P < 0.05; **P < 0.01. Table III. Multivariate analysis of intervention effect on HIV knowledge and HIV stigma Model 1: (X~M) Model 2:(X~Y) Model 3: (X, M~Y) HIV knowledge (a) HIV stigma (c) HIV stigma (c?) (b) Constant 5.318 (4.759) 3.001 (1.341) 3.363 (1.325) Age 0.015 (0.270) 0.000 (0.076) 0.005 (0.075) Gender 0.048 (0.356) 0.147 (0.099) 0.148 (0.098) School performance 0.063 (0.179) 0.029 (0.050) 0.026 (0.049) Father's education 0.244 (0.473) 0.032 (0.132) 0.026 (0.130) Mother's education 0.285 (0.447) 0.234 (0.124) 0.253 (0.123)* Family income 0.334 (0.367) 0.129 (0.102) 0.109 (0.101) Family composition 0.250 (0.540) 0.188 (0.150) 0.174 (0.148) Baseline stigmatizing attitude 0.025 (0.220) 0.122 (0.061)* 0.121 (0.060)* Baseline HIV knowledge 0.272 (0.062)** 0.031 (0.017) 0.017 (0.018) Intervention (X) 0.900 (0.342)** 0.186 (0.095)* 0.138 (0.095) Post-intervention HIV knowledge (M) — — 0.052 (0.017)** Numbers in the cells are unstandardized coefficients (SE). X: Intervention condition; M: HIV knowledge; Y: HIV stigma. * P < 0.05; **P < 0.01. X. Li et al. 428 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 Given the young age of the participants, operationalizing these theoretical constructs has always been a challenge [31]. The low Cronbach's alpha for most PMT constructs may contribute to the nonsignificant findings in the current study, although these alphas were comparable with those in the previous FOK intervention studies [31, 33]. Second, there might be an insufficient statistical power to detect the significance due to the relatively small sample size. Although the changes of most PMT constructs between the intervention group and the control group at the 6-month post-intervention did not reach statistical significance, many of them showed a trend of changes in the expected direction (e.g. decrease in scores of extrinsic rewards, increases in scores of severity, vulnerability and response efficacy). The limited intervention effect on PMT constructs may also be a result of the changes in the curriculum including the removal of condom userelated materials. The materials related to condom use were removed from the original FOK curriculum during the adaption process. Although the local schools initiated the motion of removal, the removal was developmentally justified (e.g. very few of the students were sexually experienced) and consistent with the change of the program focus (i.e. from safe sex to abstinence). It was possible that the effect of the culturally adapted curriculum was affected by the removal of the condom use materials. However, given the program focus on abstinence among adolescents of this age group, it was also unlikely that such effect will be substantial. Nevertheless, future study is needed to examine this longstanding issue of balance between the program fidelity and cultural adaptation and explore the potential effect of removing a core program component, although justified, on the effectiveness of the program in a new cultural setting or among a new population. There are several methodological limitations in this study. First, the single item measure of the stigmatizing attitude toward PLWHA might not reflect other aspects of stigma against PLWHA. Second, the sample in the current study was a convenience sample. Therefore, our ability to generalize the findings to other populations of adolescents may be limited. Third, the current study employed a quasi-experimental research design, so potential treats to the internal validity of the study may exist. Fourth, this study evaluated only the effects at 6-month post-intervention, longer term effects need to be evaluated in the future research. Fifth, most PMT scales had low reliability estimates (e.g. Cronbach's a <0.70). Future study is needed to develop culturally appropriate and psychometrically adequate measurement of PMT constructs in China and other non-Western cultural settings. Despite these potential limitations, to the best of our knowledge, the current study is one of the first efforts to implement and evaluate a theory-driven school-based HIV prevention intervention among high school students in China. The findings of the current study have several implications for the future HIV prevention efforts among adolescents in China and other developing countries. First, schoolbased HIV prevention intervention programs among adolescents need to include a focus on HIV awareness. The lack of HIV awareness (e.g. seeing HIV infection as a remote possibility) may reduce the perceptions of severity and vulnerability of contracting HIV, which in turn, may reduce one's protection motivation. Second, HIV stigma reduction should be built into the existing HIV prevention programs among adolescents. HIV-related stigma reduction intervention among children and adolescents is critical for effective HIV prevention, diagnosis, treatment and care. HIV knowledge education and stigma reduction intervention among adolescents may benefit the HIV testing and treatment that subsequently prevent the HIV epidemic. Third, the HIV prevention program among children and young adults in China needs to prepare them for future engagement in protective behaviors such as condom use by improving their knowledge in relation to vulnerability and severity of HIV/AIDS and their perceptions of self-efficacy and response efficacy of condom use. In China and other Asian countries, there is a strong social pressure against premarital sex, which may contribute the delay in sexual initiation among adolescents HIV prevention intervention among adolescents in China 429 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 and young adults [31, 44]. However, the same social pressure also may contribute to youth being unprepared to engage in protective behaviors. Given the rapid social and economic changes in China and resultant changes in youth's sexuality and personal freedom, there is an increased likelihood that these youth need to be prepared to make informed decision regarding protective sexual behaviors including use of condoms (to prevent HIV/STD and unwanted pregnancy) in the near future. While the main focus of the current research, in respond to the low rate of sexual initiation among the target population, was on abstinence rather than safe sex, future research is needed to examine the link of the improvement in HIV/AIDS knowledge and other attitudes with safe sex behaviors among these adolescents if and when they become sexually active. Finally, future research needs to reconceptualize the theoretical constructs related to HIV-related sexual risk so that they can be used to adequately assess the perceptions of adolescents and guide the development and implementation of effective HIV prevention education in China and other developing countries. Funding World AIDS Foundation (218-00-014). Acknowledgements The authors thank colleagues and graduate students at the Nanjing University Institute of Mental Health for their assistance in data collection. The authors also thank Ms Joanne Zwemer for assistance in preparing the manuscript. Conflict of interest statement None declared. References 1. Bearinger L, Sieving R, Ferguson J et al. Global perspectives on the sexual and reproductive health of adolescents: patterns, prevention, and potential. Lancet 2007; 369: 1220-31. 2. DiClemente RJ, Wingood GM, Harrington KF et al. Efficacy of an HIV prevention intervention for African American adolescent girls: a randomized controlled trial. JAMA 2004; 292: 171-9. 3. Kirby D, Laris BA, Rolleri L. Sex and HIV education programs: their impact on sexual behaviors of young people throughout the world. J Adolesc Health 2007; 40: 206-17. 4. Mullen PD, Ramı´rez G, Strouse D et al. Meta-analysis of the effects of behavioral HIV prevention interventions on the sexual risk behavior of sexually experienced adolescents in controlled studies in the United States. J Acquir Immune Defic Syndr 2002; 30: S94-105. 5. Stanton B, Li X, Ricardo I et al. A randomized, controlled effectiveness trial of an AIDS prevention program for lowincome African-American youth. Arch Pediatr Adolesc Med 1996; 150: 363-72. 6. Kirby D, Laris B. Effective curriculum-based sex and STD/ HIV education programs for adolescents. Child Dev Perspec 2009; 3: 21-9. 7. Speizer IS, Magnani RJ, Colvin CE. The effectiveness of adolescent reproductive health interventions in developing countries: a review of the evidence. J Adolesc Health 2003; 33: 324-48. 8. Harvey B, Stuart J, Swan T. Evaluation of a drama-ineducation programme to increase AIDS awareness in South African high schools: a randomized community intervention trial. Int J STD AIDS 2000; 11: 105-11. 9. Aplasca MR, Siegel D, Mandel JS et al. Results of a model AIDS prevention program for high school students in the Philippines. AIDS 1995; 9: S7-13. 10. Kinsman J, Nakiyingi J, Kamali A et al. Evaluation of a comprehensive school-based AIDS education programme in rural Masaka, Uganda. Health Educ Res 2001; 16: 85-100. 11. Pryor JB, Reeder GD, Landau S. A social-psychological analysis of HIV-related stigma. Am Behav Scientist 1999; 42: 1193-211. 12. Reidpath DD, Chan KY. HIV, stigma, and rates of infection: a rumour without evidence. PLoS Med 2006; 3: 1708-10. 13. Varas-Dı´az N, Serrano-Garcı´a I, Toro-Alfonso J. AIDSrelated stigma and social interaction: puerto Ricans living with HIV/AIDS. Qual Health Res 2005; 15: 169-87. 14. Li X, Wang H, Williams A et al. Stigma reported by people living with HIV in south central China. J Assoc Nurses AIDS Care 2009; 20: 22-30. 15. Lin X, Zhao G, Li X et al. Perceived HIV stigma among children in a high HIV-prevalence area in central China: beyond the parental HIV-related illness and death. AIDS Care 2010; 22: 545-55. 16. Li X, Zhang L, Fang X et al. Stigmatization experienced by rural-to-urban migrant workers in China: findings from a qualitative study. World Health Popul 2007; 9: 1-15. 17. Lau JTF, Tsui HY, Chan KY. Reducing discriminatory attitudes toward PLWHA in Hong Kong: an intervention study using a knowledge based, PLWHA participation and cognitive approach. AIDS Care 2005; 17: 85-101. 18. Li L, Lin C, Wu Z et al. Stigmatization and shame: consequences of caring for HIV/AIDS patients in China. AIDS Care 2007; 19: 258-63. X. Li et al. 430 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019 19. Yang H, Li X, Stanton B et al. HIV-related knowledge, stigma, and willingness to disclose: a mediation analysis. AIDS Care 2007; 18: 717-24. 20. Yang Y, Zhang K, Chan K et al. Institutional and structural forms of HIV-related discrimination in health care: a study set in Beijing. AIDS Care 2005; 17: 129-40. 21. Wu S, Li L, Wu Z et al. A brief HIV stigma reduction intervention for service providers in China. AIDS Patient Care STDs 2008; 22: 513-20. 22. Li X, Lin C, Gao Z et al. HIV/AIDS knowledge and the implications for health promotion programs among Chinese college students: geographic, gender and age differences. Health Promot Int 2004; 19: 345-56. 23. Zhang L, Li X, Shah I. Where do Chinese adolescents obtain knowledge of sex? Implications for sex education in China. Health Educ 2007; 107: 351-63. 24. Zhang L, Li X, Mao R et al. Stigmatizing attitudes towards people living with HIV/AIDS among college students in China: implications for HIV/AIDS education and prevention. Health Educ 2008; 108: 130-44. 25. DiClemente R, Crittenden C, Rose E et al. Psychosocial predictors of HIV-associated sexual behaviors and the efficacy of prevention interventions in adolescents at-risk for HIV infection: what works and what doesn't work? Psychosom Med 2008; 70: 598-605. 26. Prentice-Dunn S, Rogers RW. Protection motivation theory and preventive health: beyond the health belief model. Health Educ Res 1986; 1: 153-61. 27. Floyds D, Prentice-Dunn S, Rogers R. A meta-analysis of research on protection motivation theory. J Appl Soc Psychol 2000; 30: 407-29. 28. Lyles C, Kay L, Crepaz N et al. Best-evidence interventions: findings from a systematic review of HIV behavioral interventions for US populations at high risk, 2000-2004. Am J Public Health 2007; 97: 133-43. 29. Galbraith J, Stanton B, Boekeloo B et al. Exploring implementation and fidelity of evidence-based behavioral interventions for HIV prevention: lessons learned from the Focus on Kids diffusion case study. Health Educ Behav 2009; 36: 532-49. 30. Stanton B, Li X, Kahihuata J et al. Increased protected sex and abstinence among Namibian youth following a HIV risk-reduction intervention: a randomized, longitudinal study. AIDS 1998; 12: 2473-80. 31. Kaljee LM, Genberg B, Riel R et al. Effectiveness of a theory-based risk reduction HIV prevention program for rural Vietnamese adolescents. AIDS Educ Prev 2005; 17: 185-99. 32. Li X, Stanton B, Wang B et al. Cultural adaptation of the focus on Kids program for college students in China. AIDS Educ Prev 2008; 20: 1-14. 33. Chen X, Lunn S, Deveaux L et al. A cluster randomized controlled trial of an adolescent HIV prevention program among Bahamian youth: effect at 12 months post-intervention. AIDS Behav 2009; 13: 499-508. 34. Sheng L, Cao W. HIV/AIDS epidemiology and prevention in China. Chin Med J (Engl) 2008; 121: 1230-6. 35. Hong Y, Li X. HIV/AIDS behavioral interventions in China: a literature review and recommendation for future research. AIDS Behav 2009; 13: 603-13. 36. Wu Z, Luo W, Sullivan S et al. Evaluation of a needle social marketing strategy to control HIV among injecting drug users in China. AIDS 2007; 21: S115-22. 37. Li X, Wang B, Fang X et al. Short-term effect of a cultural adaptation of voluntary counseling and testing among female sex workers in China: a quasi-experimental trial. AIDS Educ Prev 2006; 18: 406-19. 38. Ma S, Dukers N, van den Hoek A et al. Decreasing STD incidence and increasing condom use among Chinese sex workers following a short term intervention: a prospective cohort study. Sex Transm Inf 2002; 78: 110-4. 39. Wu Z, Rou K, Jia M et al. The first community-based sexually transmitted disease/HIV intervention trial for female sex workers in China. AIDS 2007; 21: S89-94. 40. Gao MY, Wang S. Participatory communication and HIV/ AIDS prevention in a Chinese marginalized (MSM) population. AIDS Care 2007; 19: 799-810. 41. Cheng Y, Lou C, Mueller L et al. Effectiveness of a schoolbased AIDS education program among rural students in HIV high epidemic area of China. J Adolesc Health 2008; 42: 184-91. 42. Ye X, Huang H, Li S et al. HIV/AIDS education effects on behaviour among senior high school students in a mediumsized city in China. Int J STD AIDS 2009; 20: 549-52. 43. Stanton B, Black M, Feigelman S et al. Development of a culturally, theoretically and developmentally based survey instrument for assessing risk behaviors among AfricanAmerican early adolescents living in urban low-income neighborhoods. AIDS Educ Prev 1995; 7: 160-77. 44. Zhang H, Stanton B, Li X et al. Perceptions and attitudes regarding sex and condom use among Chinese college students: a qualitative study. AIDS Behav 2004; 8: 105-17. 45. Stanton B, Guo J, Cottrell L et al. The complex business of adapting effective interventions to new populations: an urban to rural transfer. J Adolesc Health 2005; 37: 163.e17-e26. 46. Baron R, Kenny D. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986; 51: 1173-82. HIV prevention intervention among adolescents in China 431 Downloaded from https://academic.oup.com/her/article-abstract/26/3/419/742344 by guest on 02 July 2019         

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This question was created from Project 1 Description https://www.coursehero.com/file/34026432/Project-1-Description/ Part

Question This question was created from Project 1 Description https://www..com/file/34026432/Project-1-Description/ Part 1 of 7 The project files can be downloaded from: https://mega.nz/#!kS4zlYBT!-hpOhEo3oqMhRKfd1ctlX_iPhDMUDsMw_jw-YN85RJQ ATTACHMENT PREVIEW Download attachment 34026432-323591.jpeg Project 1: Automatic Review Analyzer. Issued: Mon., 2/20 Due: Fri., 3/3 at 9am Introduction The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a 1 or -1 scale, representing a positive or negative review, respectively. Table 1 shows an example of two reviews from our dataset and their corresponding labels. Review Label Nasty No flavor. The candy is just red , No flavor . Just plan and chewy . I would never buy -1 them again YUMMY! You would never guess that they’re sugar-free and it’s so great that you can eat them pretty much guilt free! i was so impressed that i’ve ordered some for myself (w dark chocolate) to take to the office. These are just EXCELLENT! Table 1: Example entries from the reviews dataset. The two reviews were written by different customers describing their experience with a sugar-free candy. In order to automatically analyze reviews, you will need to complete the following tasks: . Implement and compare three types of linear classifiers: the perceptron algorithm, the average percep- tron algorithm, and the Pegasos algorithm (Section 1). Use your classifiers on the food review dataset, using some simple text features (Section 2). . Experiment with additional features and explore their impact on classifier performance (Section 3). In addition, as part of the project we will run a competition where you will attempt to classify a set of examples for which no labels are provided. You will receive extra credit if your method performs particularly well on this dataset (see below for details). 0. Setup (0 Points) For this project and throughout the course we will be using Python 3.0 with some additional libraries. We strongly recommend that you take note of how the NumPy numerical library is used in the code provided, and read through the on-line NumPy tutorial. NumPy arrays are much more efficient than Python’s native arrays when doing numerical computation. In addition, using NumPy will substantially reduce the lines of code you will need to write. 0. Download the necessary libraries and tools. Note on software: For the all the 6.036 projects, we will use python 3.0 augmented with the NumPy numerical toolbox, the matplotlib plotting toolbox, and the scikit learn machine learning toolbox though you use scikit learnRead more

This question was created from Project 1 Description https://www.coursehero.com/file/34026432/Project-1-Description/ Part

Question This question was created from Project 1 Description https://www..com/file/34026432/Project-1-Description/ Part 2/7 The project files can be downloaded from: https://mega.nz/#!kS4zlYBT!-hpOhEo3oqMhRKfd1ctlX_iPhDMUDsMw_jw-YN85RJQ ATTACHMENT PREVIEW Download attachment 34026432-323592.jpeg 1. Download project 1 . 2 ip from Stellar and unzip it in to a working directory. The zip file contains the various data files in . tsv format, along with the following python files: a project 1 . py contains various useful functions and function templates that you will use to implement your learning algorithms. – ma in . py is a script skeleton where these functions are called and you can run your experiments. a ut i ls . py contains utility functions that the staff has implemented for you. I t e st-suit e . py is a script which runs tests on a few of the methods you will implement. Note that these tests are provided to help you debug your implementation and are not necessarily representative of the autograder. 1. Implementing classifiers (55 Points) First we will implement three linear classifiers: (l) perceptron, (2) average perceptron, and (3) Pegasos. All the algorithms will run for a fixed number of iterations, T, through the training set. As a result, the parameters will be updated at most nT times, where n is the number of training examples that you are given. The average perceptron will add a modification to the original perceptron algorithm: since the basic algorithm continues updating as the algorithm runs, nudging parameters in possibly conflicting directions, it is better to take an average of those parameters as the final answer. Every update of the algorithm is the same as before. The returned parameters 0, however, are an average of the 63 across the nT steps: 4-a. 1 T — — (1) (2) u I I (n ) 9111,31 — T (9 6 -I- 9 ) All of the following functions can be found in the project 1 . py script. You will begin by implementing the hinge loss function. For this function you will be given the parameters, 6 and 90, a feature matrix, in which the rows are feature vectors and the columns are individual features, and a vector of 1/-1 labels representing the actual sentiment of the corresponding feature vector. The km row of the feature matrix, corresponds to the kth element of the labels vector. This function should return the appropriate loss of the classifier on the given dataset. [5 Points] . Now you will implement the single step update for the perceptron algorithm (implemented with 0-1 loss). You will be given the feature vector as an array of numbers, the current 9 and 60 parameters, and the correct label of the feature vector. The function should return a tuple in which the first element is the correctly updated value of B and the second element is the correctly updated value of 00. [10 Points] In this step you will implement the full perceptron algorithm. You will be given the same feature matrix and labels array as you were given in step 2. You will also be given T, the maximum number of times that you should iterate through the feature matrix before terminating the algorithm. Initialize 9 and 90 to zero. This function should return a tuple in which the first element is the final value of 9 and the second element is the value of 60. [5 Points] . You will now implement the average perceptron algorithm. This function should be constructed simi- larly to the perceptron algorithm from part 4-a, except that it should return the average values of 0 and 90. [10 Points] Hint: Tracking a moving average through loops is difficult, but tracking a sum through loops is simple.

This question was created from Project 1 Description https://www.coursehero.com/file/34026432/Project-1-Description/ Part

Question This question was created from Project 1 Description https://www..com/file/34026432/Project-1-Description/ Part 5/7 ATTACHMENT PREVIEW Download attachment 34026432-323595.jpeg and labels in main . py. Call the appropriate accuracy function to find the accuracy on the test set. Report this value in your writeup. [3 Points] ll-b. According to the largest weights (i.e. individual 6’,- values in your 9 vector), you can find out which unigrams were the most impactful ones in predicting the labels. Uncomment the relevant part in main . py and report top ten most explanatory word features in your writeup. [2 Points] 3. New Features and the challenge (20 Points) Frequently, the way the data is represented can have a significant impact on the performance of a machine learning method. Try to improve the performance of your best classifier by augmenting columns to the feature matrix. For example, you are encouraged to try the union of unigram and bigram features. Using the same example from the previous section, the final dictionary would be (M ary, Zoves, apples, red, M wry loves, loves apples, Red apples) and the feature representations would be (1, 1, 1,0, 1, 1, 0) and (0, 0, 1, 1,0, 0, 1). Some additional features that you might want to explore are: 0 Length of the text 0 Occurrence of all-cap words (e.g. “AMAZING”, “DON’T BUY THIS”) 0 Word embeddings Besides adding new features, you can also change the original unigram feature set. For example, 0 Remove common, or stop, words from the unigram-bigram dictionary (e.g., words such as “the”, “to”, G‘for”) 0 Threshold the number of times a word should appear in the dataset before adding them to the dictionary. For example, words that occur less than three times across the train dataset could be considered irrelevant and thus can be removed. This lets you reduce the number of columns that are prone to overfitting. We have provided you the list of stop words in the file st opwo rds . txt. There are also many other things you could change when training your model: a Normalize Feature Vectors – Because the feature vectors indicate the occurrence of each word in the reviews, they will be of varying magnitude (i.e. a longer review will have a lot more Is). This can cause a the Hinge Loss to be extremely high for longer reviews because the value of 9 – as“) will be much larger for a larger as“). Normalizing the feature vectors (Le. scaling them so their magnitudes are 1) can potentially solve this problem 0 Change Pegasos’s learning rate from 1/ Jr? to some other function – Why should we choose 1/x/t as the learning rate each iteration? Try playing around with this function of t and consider choosing something different. 0 Sampling – The Pegasos algorithm discussed in class functions by randomly selecting a feature vector each step. This does not ensure that the algorithm uses your entire set of training data, and it may end up using certain vectors many times (because they are randomly selected often). Instead of doing this, you may wish to randomly perrnute your feature vectors to make sure you see all examples at some

This question was created from Project 1 Description https://www.coursehero.com/file/34026432/Project-1-Description/ Part

Question This question was created from Project 1 Description https://www..com/file/34026432/Project-1-Description/ Part 3/7 ATTACHMENT PREVIEW Download attachment 34026432-323593.jpeg 5. Next you will implement the single step update for the Pegasos algorithm. This function is very similar to the function that you implemented in step 3, except that it should utilize the Pegasos parameter update rules instead of those for perceptron. The function will also be passed a A and 77 value to use for updates. [15 Points] 6. Finally you will implement the full Pegasos algorithm. You will be given the same feature matrix and labels array as you were given in step 2. You will also be given T, the maximum number of times that you should iterate through the feature matrix before terminating the algorithm. Initialize B and 60 to zero. For each update, set 77 = i where t is a counter for the number of updates performed so far (between 1 and nT inclusive). This function should return a tuple in which the first element is the final value of 0 and the second element is the value of 00. [5 Points] 7. Once you have completed the implementation of the 3 learning algorithms, you should qualitatively ver- ify your implementations. In main . py we have included a block of code that you should uncomment. This code loads a 2D dataset from toy_data . txt, and trains your models using T = 10, A = 0.2. main . py will compute 9 and 90 for each of the learning algorithms that you have written. Then, it will call pl ot _toy_data to plot the resulting model and boundary. Why do these algorithms provide different decision boundaries? Hand in the plots obtained for each algorithm along with a short paragraph answering the question in your pdf writeup. [5 Points] 2. The automatic review analyzer (35 Points) Now that you have verified the correctness of your implementations, you are ready to tackle the main task of this project: building a classifier that labels reviews as positive or negative using text-based features and the linear classifiers that you implemented in the previous section. The Data The data consists of several reviews, each of which has been labeled with — 1 or 1, corresponding to a nega- tive or positive review, respectively. The original data has been split into four files: reviews_t rain . tsv (4000 examples), reviews _val i dat ion . t sv (500 examples), reviews _te st . t sv (500 examples) and reviews_submit .tsv (500 examples). Only the first three sets are labeled—you will use them to train, tune, and evaluate your classifier. Once you have optimized your method, you will use it to predict labels for the submit data, which you will submit along with your code and answers. To get a feel for how the data looks, we suggest first opening the files with a text editor, spreadsheet program, or other scientific software package. Translating reviews to feature vectors We will convert review texts into feature vectors using a bag of words approach. We start by compiling all the words that appear in a training set of reviews into a dictionary, thereby producing a list of cl unique words. We can then transform each of the reviews into a feature vector of length d by setting the it” coordinate of the feature vector to 1 if the it“ word in the dictionary appears in the review or zero, otherwise. For instance, consider two simple documents “Mary loves apples” and “Red apples”. In this case, the dictionary is the set {Mary, loves, apples, red}, and the documents are represented as (1, 1, 1,0) and (0,0, 1, 1). A bag of words model can be easily expanded to include phrases of length m. A unigram model is the case for which m = 1. In the example, the unigram dictionary would be (M ary, loves, apples, red). In the bigram case, m = 2, the dictionary is (M an; loves, loves apples, Red apples), and representations for each sample are (l, 1,0), (0,0,1). In this section, section 2, you will only use the unigram word features. Read more

This question was created from Project 1 Description https://www.coursehero.com/file/34026432/Project-1-Description/ Part

Question This question was created from Project 1 Description https://www..com/file/34026432/Project-1-Description/ Part 4/7 ATTACHMENT PREVIEW Download attachment 34026432-323594.jpeg These functions are already implemented for you in the bag of words function. In utils . py, we have supplied you with the load_data function, which can be used to read the .tsv files and returns the labels and texts. We have also supplied you with the bag of words function in project1 . py, which takes the raw data and returns dictionary of unigram words. The resulting dictionary is an input to extract bow- feature_vectors which computes a feature matrix of ones and zeros that can be used as the input for the classification algorithms. Using the feature matrix and your implementation of learning algorithms from Section 1, you will be able to compute 0 and 00. For this section, your tasks are the following: 8. Implement the classify function in the file project1 . py . This function takes 3 parameters, the feature matrix, the 0 parameter vector found by your learning algorithms, and the Go offset. The function should return an array of the predicted classifications of the data points in the feature matrix. [10 Points] 9-a. We have supplied you with an accuracy function that can be found in the projectl.py file. This function takes a numpy array of predicted labels and a numpy array of actual labels and returns the pre- diction accuracy. You should use this function along with the functions that you have implemented thus far in order to implement the percept ron accuracy, average-perceptron accuracy, and pegasos_accuracy functions. All of the methods take a T value, and the Pegasos method takes a ) value, as well. The functions should first train themselves using the supplied train data and then compute the classification accuracy on both the train and validation data. The return values should be a tuple where the first value is the training accuracy and the second value is the validation accuracy. 9-b. When these functions are complete, uncomment the relevant lines in main . py and report the train- ing and validation accuracies of each algorithm with T = 10 and ) = 0.01 in your pdf writeup (the ) value only applies to Pegasos). 10. To improve the performance of your methods, you will need to tune (i.e. optimize) the hyper-parameters T and A. To this end, you will train your classifiers with several values of A and T, each time using the validation data to assess the performance. Reasonable values to try are 1 = [0.01, 0.1, 0.2, 0.5, 1] and T = [1, 5, 10, 15, 25, 50, 100]. For the Pegasos algorithm, you can first fix

This question was created from Project 1 Description https://www.coursehero.com/file/34026432/Project-1-Description/ Part

Question This question was created from Project 1 Description https://www..com/file/34026432/Project-1-Description/ Part 6/7 ATTACHMENT PREVIEW Download attachment 34026432-323596.jpeg point in the algorithm (Note: be careful that you keep the labels and feature vectors aligned when doing this E). Another interesting idea is to consider batching your training examples into subgroups (called mini- batches), each with positive and negative examples, so you ensure you never accidentally look at too many positive or negative data points in a row (as this will throw off your 9 by a lot). 0 Lastly, be creative: Try anything that can help you understand the sentiment of a review. It’s worth looking through the dataset and coming up with some features that may help your model. Remember that not all features will actually help so its worth experimenting with some simpler ones before trying anything too complicated Note: If you wish to read more on Pegasos, feel free to read their paper. 12. You should now experiment with at least one additional improvement (from the suggested improve- ments above or you can come up with your own) and compare the performance of your method to your results with the original features. You can modify functions bag_o f _words and extract _bow_ f eature_ve ct ors should you improve the original bag-of-words features. NOTE: Section 2 de- pends on these functions, so if you wish to reproduce section 2 results, use the original function implementations. For this reason you may wish to create our own versions of these functions instead of directly modifying the original. To augment any additional feature columns besides bag- of—words, you can do so by implementing ext racLaddit ional ieatures. Again, you should use the validation set to choose values for the parameters and report the error on the test set. Your report should contain: a A clear explanation of how you changed the feature set, and why you think the features you chose might be useful. Include the code you use for this purpose in extract _addit ionaLfeatures. o A description of the experiment you conducted to compare it to the original feature-computation method, and the results of that experiment. Include plots if applicable. It’s okay if it turns out that your new method performs worse than the original one—just explain and document your results. It is very common that a feature you add will worsen your model, so do not be discouraged by this. If you wish to improve your model as much as possible, it’s worth testing a few different ideas and seeing if they help or not! [20 Points] 13. (Extra Credit) Now, after hours of twiddling features, classifiers, and hyperparameters, it is time to put your machine learning skills to the test by classifying the reviews in reviewasubmit . tsv for which the labels are not provided. Modify this section’s code as directed in main . py to populate reviews_submit . tsv with your predictions. Including this file in your project submission will be your entry into the (mandatory) class competition! You will receive 3 points of extra credit if the accuracy of your submitted labels is above the staff baseline (the staff is busy and probably won’t spend as much time as you). You will receive 10 points extra credit and looks of jealousy from all of your friends if your predictions are the best in the class. The top performing scores will be posted on Piazza. Read more

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Question What are at least five different types of security measures pertaining to the EHR, including the components of each security measure?

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