What features of drug treatment programs help, or not, with access? a qualitative study of the perspectives of family members and community-based organization staff in Atlantic Canada
Bibliographic record
Abstract
BACKGROUND: Withdrawal management and opioid agonist treatment (OAT) programs help to reduce some of the harms experienced by people who use substances (PWUS). There is literature on how features of drug treatment programs (e.g., policies and practices) are helpful, or not helpful, to PWUS when seeking access to, or in, treatment. There is, however, relatively little literature based on the perspectives of family members/family of choice of PWUS and community-based organization staff within the context of Atlantic Canada. This paper explored the perspectives of these two groups on what was helpful, or not, about drug treatment programs in Atlantic Canada in terms of supporting access to, and retention in, treatment. METHODS: One-on-one qualitative telephone interviews were conducted in 2020 with the two groups. Interviews focused on government-funded withdrawal management and OAT programs. Data were coded using a qualitative data management program (ATLAS.ti) and analyzed inductively for key themes/subthemes using grounded theory techniques. RESULTS: Fifteen family members/family of choice and 16 community-based organization staff members participated (n = 31). Participants spoke about features of drug treatment programs in various places, and noted features that were perceived as helpful (e.g., quick access), as well as not helpful (e.g., wait times, programs located far from where PWUS live). Some participants provided their perceptions of how PWUS felt when seeking or accessing treatment. A number of participants reported taking various actions to help support access to treatment, including providing transportation to programs. A few participants also provided suggestions for change to help support access and retention such as better alignment of mental health and addiction systems. CONCLUSIONS: Participants highlighted several helpful and not helpful features of drug treatment programs in terms of supporting treatment access and retention. Previous studies with PWUS and in other places have reported similar features, some of which (e.g., wait times) have been reported for many years. Changes are needed to reduce barriers to access and retention including the changes recommended by study participants. It is critical that the voices of key groups, (including PWUS) are heard to ensure treatment programs in all places support access and retention.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".