ObjectiveTo assess the correlates for bisexual behaviors, HIV knowledge, and HIV/AIDS-related stigmatizing/discriminatory attitudes among men who have sex with men (MSM).MethodsA cross-sectional survey among MSM was conducted in 2011 to provide demographics, sexual behaviors, HIV knowledge, HIV/AIDS-related stigmatizing/discriminatory attitudes, and services in Jinan, Qingdao, and Yantai of Shandong Province of China.ResultsOf 1230 participants, 82.8% were single, 85.7% aged <35 years, and 47.2% received college or higher education. There were 28.6% MSM who reported to be married or cohabitating or ever had sex with woman in the past 6 months (P6M). 74.5% had ≥6 HIV-related knowledge score. The average total score of stigmatizing/discriminatory attitude was 37.4±4.4(standard deviation). Bisexual behavior was independently associated with higher levels of HIV/AIDS-related stigma/discrimination(AOR = 1.1, 95% CI:1.0–1.1), older age(AOR = 1.2, 95%CI:1.1–1.2), and lower HIV-related knowledge score(AOR = 1.6, 95%CI:1.2–2.2). HIV knowledge score ≥6 was independently associated with lower levels of HIV/AIDS-related stigma/discrimination(AOR = 1.3, 95%CI:1.2–1.3), less bisexual behaviors(AOR = 0.6, 95%CI:0.5–0.9), ever received a test for HIV in the past 12 months (P12M)(AOR = 3.2, 95%CI:2.3–4.5), college or higher level education(AOR = 1.9, 95%CI:1.4–2.6), consistent condom use with men in P6M(AOR=6.9, 95%CI:4.6–10.6), recruited from internet or HIV testing sites(AOR = 11.2, 95%CI:8.0–16.1) and bars, night clubs, or tea houses(AOR = 2.5, 95%CI:1.7–4.8). Expressing higher levels of HIV/AIDS-related stigmatizing/discriminatory attitudes was independently associated with bisexual behaviors(Aβ = 0.9, 95%CI:0.4–1.4), lower HIV-related knowledge score(Aβ = 3.6, 95%CI:3.0–4.1), the number of male sex partners in the past week ≥2(Aβ = 1.4, 95%CI:1.0–1.9), unprotected male anal sex in P6M(Aβ = 1.0, 95%CI:0.5–1.6), and inversely associated with ever received HIV test(Aβ = 1.4, 95%CI:0.8–2.0) and peer education in P12M(Aβ = 1.4, 95%CI:0.9–1.9).ConclusionHIV/AIDS-related stigmatizing/discriminatory attitudes were associated with bisexual behaviors, low HIV testing rate, lower HIV-related knowledge and risk behaviors. This study called for innovative programs that would reduce HIV/AIDS-related stigmatizing/discriminatory attitudes and bisexual behaviors and improve the uptake of prevention service among MSM.
Just-in-time software defect prediction (JIT-SDP) is an active topic in software defect prediction, which aims to identify defect-inducing changes. Recently, some studies have found that the variability of defect data sets can affect the performance of defect predictors. By using local models, it can help improve the performance of prediction models. However, previous studies have focused on module-level defect prediction. Whether local models are still valid in the context of JIT-SDP is an important issue. To this end, we compare the performance of local and global models through a large-scale empirical study based on six open-source projects with 227417 changes. The experiment considers three evaluation scenarios of cross-validation, cross-project-validation, and timewise-cross-validation. To build local models, the experiment uses the k-medoids to divide the training set into several homogeneous regions. In addition, logistic regression and effort-aware linear regression (EALR) are used to build classification models and effort-aware prediction models, respectively. The empirical results show that local models perform worse than global models in the classification performance. However, local models have significantly better effort-aware prediction performance than global models in the cross-validation and cross-project-validation scenarios. Particularly, when the number of clusters k is set to 2, local models can obtain optimal effort-aware prediction performance. Therefore, local models are promising for effort-aware JIT-SDP.
Our report suggests, at the current rate, Shandong Province has to accelerate HIV care efforts to close disparities in HIV care and achieve the 90-90-90 goals equitably.
Community smells appear in sub-optimal software development community structures, causing unforeseen additional project costs, e.g., lower productivity and more technical debt. Previous studies analyzed and predicted community smells in the granularity of community sub-groups using socio-technical factors. However, refactoring such smells requires the effort of developers individually. To eliminate them, supportive measures for every developer should be constructed according to their motifs and working states. Recent work revealed developers' personalities could influence community smells' variation, and their sentiments could impact productivity. Thus, sentiments could be evaluated to predict community smells' occurrence on them. To this aim, this paper builds a developer-oriented and sentiment-aware community smell prediction model considering 3 smells such as Organizational Silo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developer quitted the community after being affected by any smell. The proposed model achieves cross-and within-project prediction F-Measure ranging from 76% to 93%. Research also reveals 6 sentimental features having stronger predictive power compared with activeness metrics. Imperative and indicative expressions, politeness, and several emotions are the most powerful predictors. Finally, we test statistically the mean and distribution of sentimental features. Based on our findings, we suggest developers should communicate in a straightforward and polite way.
Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. The effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify defective software changes in limited software testing resources. Although many methods have been proposed to solve the JIT-SDP, the effort-aware prediction performance of the existing models still needs to be further improved. To this end, we propose a differential evolution (DE) based supervised method DEJIT to build JIT-SDP models. Specifically, first we propose a metric called density-percentile-average (DPA), which is used as optimization objective on the training set. Then, we use logistic regression (LR) to build a prediction model. To make the LR obtain the maximum DPA on the training set, we use the DE algorithm to determine the coefficients of the LR. The experiment uses defect data sets from six open source projects. We compare the proposed method with state-of-the-art four supervised models and four unsupervised models in cross-validation, cross-project-validation and timewise-cross-validation scenarios. The empirical results demonstrate that the DEJIT method can significantly improve the effort-aware prediction performance in the three evaluation scenarios. Therefore, the DEJIT method is promising for the effort-aware JIT-SDP.
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