Injecting on the Island: a qualitative exploration of the service needs of persons who inject drugs in Prince Edward Island, Canada
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated the service needs of persons who inject drugs (PWID) who live in less populated regions of Canada. With access to fewer treatment and harm reduction services than those in more urban environments, the needs of PWID in smaller centres may be distinct. As such, the present study examined the needs of PWID in Prince Edward Island (PEI), the smallest of Canada's provinces. METHODS: Eight PWID were interviewed about the services they have accessed, barriers they faced when attempting to access these services, and what services they need that they are not currently receiving. RESULTS: Participants encountered considerable barriers when accessing harm reduction and treatment services due to the limited hours of services, lengthy wait times for treatment, and shortage of health care practitioners. They also reported experiencing considerable negativity from health care practitioners. Participants cited incidences of stigmatisation, and they perceived that health care practitioners received insufficient training related to drug use. Recommendations for the improvement of services are outlined. CONCLUSIONS: The findings indicate that initiatives should be developed to improve PWID's access to harm reduction and treatment services in PEI. Additionally, health care practitioners should be offered sensitisation training and improved education on providing services to PWID. The findings highlight the importance of considering innovative alternatives for service provision in regions with limited resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".