The impact of determinants of health on the relationship between stigma and health in people living with HIV
Bibliographic record
Abstract
Determinants of health are important drivers of health states, yet there is little work examining their role in the relationship between HIV stigma and health. This study uses moderation analysis to examine how determinants of health affect the relationship between enacted, internalized, and anticipated stigma and mental health. Quantitative data was collected on 337 participants in Ontario, Canada at baseline (t1) between August 2018 and September 2019 and at follow-up (t2) between February 2021 and October 2021. Separate moderation models were created with each determinant of health (age, gender, sexual orientation, ethnicity, geographic region, education, employment, and basic needs) acting as the moderator between types of stigma at t1 and mental health at t2. Age was a significant moderator for the relationship between internalized and enacted stigma at t1 and mental health at t2. Region was a moderator for enacted and anticipated stigma and mental health. Sexual orientation was a moderator for anticipated stigma and mental health. Lastly, having basic needs was a moderator for enacted and anticipated stigma and mental health. Our findings suggest that intervention strategies may be more effective by incorporating supports for these determinants of health in addition to stigma reduction to improve mental health.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".