Incarceration, Addiction and Harm Reduction: Inmates Experience Injecting Drugs in Prison
Bibliographic record
Abstract
Within Canadian prisons HIV/AIDS is becoming more common among inmates. While injection drug use in correctional facilities is documented to be a problem, qualitative research into the HIV risks faced by inmates is lacking. The goal of this research was to qualitatively examine HIV risk associated with injecting inside British Columbia prisons. A sample of 26 former male inmates who had recently used drugs within correctional facilities were recruited from a ongoing cohort study of injection drug users in Vancouver, Canada. Data for this study were collected through in-depth interviews conducted in 2001/2002. Analysis of these data involved identifying emergent themes and then exploring these central concepts in further interviews to confirm the accuracy of interpretation. The harms normally associated with drug addiction, and injection drug use are exacerbated in prison. Interpersonal relationships and the possession of exchangeable resources determine access to scarce syringes. The scarcity of syringes has resulted in patterns of sharing amongst large numbers of persons. Continual reuse of scarce syringes poses serious health hazards and bleach distribution is an inadequate solution. The findings of this study emphasize the need for effective harm reduction programs that provide an appropriate response to the problem of injection drug use among inmates.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".