Using concept mapping to inform the development of a transitional reintegration intervention program for formerly incarcerated people with HIV
Bibliographic record
Abstract
BACKGROUND: Accessing HIV-related care is challenging for formerly incarcerated people with HIV. Interventions informed by the perspectives of these individuals could facilitate engagement with care and address competing priorities that may act as barriers to this process. METHODS: We used concept mapping to identify and prioritize the main obstacles to engaging with HIV-related care following prison release. In brainstorming sessions, formerly incarcerated people with HIV generated responses to a focused prompt regarding the main barriers to reengaging with care. These were consolidated in 35 statements. Next, participants sorted the consolidated list of responses into groups and rated each from lowest to highest in terms of its importance and feasibility of being addressed. We used cluster analysis to generate concept maps that were interpreted with participants. RESULTS: Overall, 39 participants participated in brainstorming sessions, among whom 18 returned for rating and sorting. Following analysis, a seven-cluster map was generated, with participants rating the 'Practical Considerations' (e.g. lack of transportation from prison) and 'Survival Needs' (e.g. securing housing and food) clusters as most important. Although ratings were generally similar between women and men, women assigned greater importance to barriers related to reconnecting with children. CONCLUSIONS: Using concept mapping, we worked with formerly incarcerated people with HIV to identify and prioritize key challenges related to accessing health and social services following prison release. Transitional intervention programs should include programs and processes that address meeting basic subsistence needs and overcoming logistical barriers related to community re-entry.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".