A cross-sectional description of social capital in an international sample of persons living with HIV/AIDS (PLWH)
Bibliographic record
Abstract
BACKGROUND: Social capital refers to the resources linked to having a strong social network. This concept plays into health outcomes among People Living with HIV/AIDS because, globally, this is a highly marginalized population. Case studies show that modifying social capital can lead to improvements in HIV transmission and management; however, there remains a lack of description or definition of social capital in international settings. The purpose of our paper was to describe the degree of social capital in an international sample of adults living with HIV/AIDS. METHODS: We recruited PLWH at 16 sites from five countries including Canada, China, Namibia, Thailand, and the United States. Participants (n = 1,963) completed a cross-sectional survey and data were collected between August, 2009 and December, 2010. Data analyses included descriptive statistics, factor analysis, and correlational analysis. RESULTS: Participant's mean age was 45.2 years, most (69%) identified as male, African American/Black (39.9%), and unemployed (69.5%). Total mean social capital was 2.68 points, a higher than average total social capital score. Moderate correlations were observed between self-reported physical (r = 0.25) and psychological condition (r = 0.36), social support (r = 0.31), and total social capital. No relationships between mental health factors, including substance use, and social capital were detected. CONCLUSIONS: This is the first report to describe levels of total social capital in an international sample of PLWH and to describe its relationship to self-reported health in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".