How the Interaction of Childhood Sexual Abuse and Gender Relates to HIV Risk Practices among Urban-Dwelling African Americans
Bibliographic record
Abstract
PURPOSE: Previous research has demonstrated that HIV risk practices often differ based on gender and on whether or not people experienced sexual abuse during their formative (i.e., childhood and adolescence) years. The interaction of these two factors, which is the focus of this paper, has received limited attention. METHODS: Based on a model derived from Social Disorganization Theory and Syndemics Theory, interviews were conducted between 2009 and 2012 with 1,864 African American adults residing in Atlanta, Georgia in 80 strategically-chosen consensus block groups. RESULTS: Based on multiple regression and structural equation analyses, the interaction of sexual abuse and gender was found to be a significant predictor of involvement in (un)protected sex. The interaction of sexual abuse and gender also was related to condom use self-efficacy, which was one of the strongest factors underlying (un)protected sex. CONCLUSION: The relationship of sexual abuse history and gender is relevant in the understanding of HIV risk practices. The interaction of these factors with one another and with other relevant influences that shape people's HIV risk profiles is complex. The Syndemics Theory approach used to conceptualize the relationships among relevant variables in this study is an effective way of trying to understand and address HIV risk practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".