Key challenges in providing services to people who use drugs: The perspectives of people working in emergency departments and shelters in Atlantic Canada
Bibliographic record
Abstract
AIMS: Many people who use drugs (PWUD) have multiple health and social needs, and research suggests that this population is increasingly accessing emergency departments (EDs) and shelters for health care and housing. This qualitative study explored the practices of those working in EDs and shelters when providing services to PWUD, with a particular focus on key challenges in service provision. METHODS: EDs and shelters were conceptualized as 'micro environments' with various components (i.e. social, physical and resource). One-on-one interviews were conducted with 57 individuals working in EDs and shelters in Atlantic Canada. FINDINGS: The social, physical and resource environments within some EDs and shelters are key forces in shaping the challenges facing those providing services. For example, the social environments within these settings are focused on acute health care in the case of EDs, and housing in the case of shelters. These mandates do not encompass the complex needs of many PWUD. Resource issues within the wider community (e.g. limited drug treatment spaces) further contribute to the challenges. CONCLUSIONS: Structural issues, internal and external to EDs and shelters need to be addressed to reduce the challenges facing many who work in these settings when providing services to PWUD.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".