Perspectives of Latinas Regarding the Influence of Socio-Cultural Factors on HIV Prevention
Bibliographic record
Abstract
Despite promising declines in new HIV diagnoses, Latinas continue to experience elevated rates of HIV in the United States. Their increased risk of HIV infection is influenced by socio-cultural factors that hinder effective prevention efforts. The objective of this study was to elicit the perspectives of Latinas about the influence of socio-cultural factors on HIV prevention. A qualitative description method was conducted with 33 women who participated in focus groups in South Florida. A conventional content analysis was used, with coding categories derived directly from the participants’ narratives. Analysis yielded three categories related to socio-cultural factors that influence HIV transmission: (1) Marianismo , (2) Machismo , and (3) Familismo . The findings revealed power imbalances in Latinas’ intimate relationships, shaped by these three socio-cultural factors that continue to influence sexual health behaviors and increase HIV risk. The findings also revealed that while power imbalances exist, Latinas also exercise autonomy, support their families, or find alternative ways to access prevention resources. These behaviors reflect ongoing sociocultural influences that shape decision-making and health engagement. It is therefore critical to consider these evolving dynamics when developing HIV prevention interventions that are responsive to the unique socio-cultural contexts affecting Latinas.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".