A qualitative study examining the experiences of people who use drugs in supportive housing in London, Ontario
Bibliographic record
Abstract
Background Since 2016, London, Ontario has experienced increasing numbers of opioid overdoses and overdose related deaths. Supportive housing agencies have been especially impacted, experiencing an 890% increase in opioid overdose from 2018-2021. A coalition was formed between London housing agencies and our research team to better understand the perspective of precariously housed participants who use drugs on existing substance use and overdose related policies within housing agencies. We aimed to center participant voices in policy development efforts.Methods Using a community-based participatory research framework, 17 participants were interviewed at three housing agencies. Drawing on qualitative descriptive methods, transcripts were subject to content analysis and three themes were identified.Results Themes included (1) conflicts between agency rules and resident realities, (2) safe use accessibility, (3) trust and understanding key to facilitating harm reduction.Conclusion Trust and understanding were weaved across all themes and mediated participants’ opioid overdose experience and risk of overdose related deaths. We recommend interventions to rebuild trust and foster understanding alongside harm reduction-based policies to reduce overdose related deaths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".