Features of drug addiction treatment programs in Atlantic Canada that help (or not) with access and retention: A qualitative study
Bibliographic record
Abstract
Access to government-funded addiction treatment programs can reduce harms experienced by people who use substances (PWUS). There is some research on what features (e.g., policies and practices) of treatment programs help or do not help with access; however, not much is known about program directors' and physicians' perspectives of these features in Atlantic Canada. One-on-one semi-structured qualitative interviews were conducted with program directors and physicians working in government-funded addiction treatment programs in Atlantic Canada in 2021. Interview questions focused on perspectives of program features that helped or not with access and retention, including perspectives on changes due to COVID-19. Data were coded and analyzed using grounded theory techniques to develop themes and subthemes. Fourteen individuals were interviewed. They identified several features that helped (e.g., quick access) or did not help (e.g., wait times) with access and retention. Participants shared some features that changed due to COVID-19, including some that helped (e.g., virtual services) and did not (e.g., limited program spaces). Participants suggested changes that could support access and retention, including better linkages to mental health supports. This paper highlights program directors' and physicians' perspectives on how program features inform access and retention in Atlantic Canada. Findings on changes made during COVID-19 point to the need to maintain the changes that were helpful and implement additional changes to better support access for more PWUS. To support the implementation and sustainability of these changes, more resources must be invested.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".