Harm reduction through housing first: an assessment of the Emergency Warming Centre in Inuvik, Canada
Bibliographic record
Abstract
BACKGROUND: This research examines the effectiveness of an Emergency Warming Centre (EWC) in Inuvik, Canada, at reducing rates of morbidity and mortality for homeless persons with concurrent disorders (mental health problems and addictions). Inuvik is a small town of approximately 3500 residents, with over 65% being Aboriginal. The town is situated on the Beaufort Delta in the Western Canadian Arctic and is subject to oil and gas extraction-based boom and bust economic cycles. The centre provided food and accommodation for those under the influence of alcohol or drugs who had no other place to stay. METHODS: Qualitative interviews about users' experiences at the centre were conducted with guests, as they were called, centre staff and other key stakeholders in autumn 2014 and spring 2015. Samples of (9) respondents and (7) stakeholders provided significant information about the importance of the EWC. The content of the qualitative data with guests and stakeholders were analyzed for emergent themes. RESULTS: Several emergent themes and subthemes related to participants' experiences at the EWC and success of the centre. Overall, the results showed that guests benefitted from a safe place to stay and felt better about their overall health. CONCLUSIONS: Compared with research on wet shelters in New Zealand, Great Britain and the US, this research reveals that harm reduction-based models for homeless persons with concurrent disorders require significant investments in infrastructure, which are not readily available. Yet, the lessons learned from these jurisdictions might be extrapolated to communities like Inuvik to develop alternative housing strategies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".