The development of social capital in a peer-led mHealth cognitive behavioral antiretroviral therapy adherence intervention for HIV + adolescents and young adults
Bibliographic record
Abstract
Adherence Connection for Counseling, Education, and Support (ACCESS)-I is a peer-led mHealth antiretroviral therapy adherence intervention for adolescents and young adults living with HIV who are in treatment but have detectable viral loads. Participants received five online sessions with peer health coaches who followed a structured intervention manual. Peers maintained intervention fidelity but also engaged in casual discussion that was not directly related to ART adherence or HIV. We conducted a qualitative analysis of the casual interactions that occurred during the ACCESS I intervention. Sessions were transcribed and coded, and these casual interactions were then coded into 10 subcodes to document their content, and also coded for three types of social capital - emotional, informational, and instrumental. Emotional and Informational social capital codes were the most common, while instrumental codes were rare. Activities was the most common topic overall, while encouragement was more common in emotional social capital narratives and personal experience was more common in informational social capital narratives. These casual interactions may strengthen peer-participant relationships, building social capital that could then be used to encourage positive behavior change. Although social capital was not directly measured, these analyses illustrate the value of attending to seemingly casual interactions in peer-led 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".