Access to health and social services for IDU: The impact of a medically supervised injection facility
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Injection drug users (IDU) often experience barriers to conventional health-care services, and consequently might rely on acute and emergency services. This study sought to investigate IDU perspectives regarding the impact of supervised injection facility (SIF) use on access to health-care services. DESIGN AND METHODS: Semi-structured qualitative interviews were conducted with 50 Vancouver-based IDU participating in the Scientific Evaluation of Supervised Injecting cohort. Audio-recorded interviews elicited IDU perspectives regarding the impact of SIF use on access to health and social services. Interviews were transcribed verbatim and a thematic analysis was conducted. RESULTS: Fifty IDU, including 21 women, participated in this study. IDU narratives indicate that the SIF serves to facilitate access to health care by providing much-needed care on-site and connects IDU to external services through referrals. Participants' perspectives suggest that the SIF has facilitated increased uptake of health and social services among IDU. DISCUSSION AND CONCLUSIONS: Although challenges related to access to care remain in many settings, SIF have potential to promote health by facilitating enhanced access to health-care and social services through a model of care that is accessible to high-risk IDU.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".