Managing the move: HIV and coping for men moving to Johannesburg
Bibliographic record
Abstract
Men in South Africa are disproportionately underrepresented in HIV care, and men who migrate face challenges using HIV services. Positive coping skills are critical for effective healthcare decision-making, yet little is known how men’s HIV status and mobility-related stressors affect their coping strategies. Johannesburg is home to ∼5 million people who have relocated from elsewhere, mostly men. From February-April 2024, we conducted a cross-sectional survey among men who moved to Johannesburg, including questions on HIV status and the Soweto Coping Scale, with problem-focused/emotional and religious coping scores as summed responses. We fitted linear regression models using data from 157 men with complete responses to assess associations between coping scores with HIV status and mobility variables. Religious coping score was 2.21 points higher among men living with HIV versus others (p = 0.026). Religious (2.34 points higher; p = 0.024) and problem-focused/emotional (2.93 points higher; p = 0.022) scores were higher among men with citizenship/permanent versus those without. Targeted coping support for men without permanent residency status may improve engagement in HIV care. Although our findings are not generalizable to all migrant population, further research may help to understand how religious beliefs impact coping and clinical outcomes among South African men living with HIV to inform interventions.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".