Transitional care for formerly incarcerated persons with HIV: protocol for a realist review
Bibliographic record
Abstract
BACKGROUND: Little is known about the mechanisms that influence the success or failure of programs to facilitate re-engagement with health and social services for formerly incarcerated persons with HIV. This review aims to identify how interventions to address such transitions work, for whom and under what circumstances. METHODS: We will use realist review methodology to conduct our analysis. We will systematically search electronic databases and grey literature for English language qualitative and quantitative studies of interventions. Two investigators will independently screen citations and full-text articles, abstract data, appraise study quality and synthesize the literature. Data analysis will include identifying context-mechanism-outcome configurations, exploring and comparing patterns in these configurations, making comparisons across contexts and developing explanatory frameworks. DISCUSSION: This review will identify mechanisms that influence the success or failure of transition interventions for formerly incarcerated individuals with HIV. The findings will be integrated with those from complementary qualitative and quantitative studies to inform future interventions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016040054.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.103 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.085 | 0.014 |
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".