How Participants Experience London Housing Agencies’ Substance Use Policies
Bibliographic record
Abstract
In London, Ontario, the number of opioid overdoses (OO) and overdose-related deaths (ORD) in the homeless population has increased rapidly in the last several years. Since 2018, the number of OO reported by London emergency shelter and housing agencies through the Homeless Facilities Information System has increased by 790%. In response to this, Western University was approached by several housing and emergency shelter agencies that were seeking consistent policies to reduce overdoses. In collaboration with those agencies, this community-based research project aimed to better understand the perspective of participants (i.e., service users) at these agencies regarding current substance use and overdose-related policies in place and how they impact their lives. We conducted sixteen semi-structured interviews with participants who use drugs and are precariously housed at the three participating emergency shelters and housing agencies. These three agencies each had unique policies and catered to different demographics. Interviews were analyzed using qualitative description methods, including content and thematic analysis, to identify broad themes associated with participants’ experiences at emergency shelters and housing agencies in London. The major themes will inform local policies related to shelter substance use and precarious housing. This project is part of a broader series of projects which aims to establish consistent and comprehensive drug policies that include perspectives from participants, volunteers, and staff in London’s housing and emergency shelter agencies.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".