Exploring harm reduction among Canadian Veterans experiencing homelessness
Bibliographic record
Abstract
Introduction: In Canada, Veteran homelessness is an increasing concern. It is estimated that approximately 2,950 Veterans experienced homelessness in 2016. Some may also have substance use disorders, which has been linked to loss of housing and homelessness many years after exiting military service. A strategy that facilitates housing stability and reduces the harms of substance use is harm reduction. This study explored how Veterans who have experienced homelessness perceive and experience harm reduction. Methods: This study was a secondary analysis of data collected from the Canadian Model for Housing and Support of Veterans Experiencing Homelessness study that evaluated a Veteran-specific housing model in four Canadian cities. In the primary study, 78 participants received housing and related programming intervention. Focus groups with Veterans occurred at each of the four housing sites from 2012 to 2014. Across all sites, 24 Veterans attended both the 2012 and 2013 focus groups. A total of 39 Veterans attended the final focus groups in 2014. The authors conducted a thematic analysis of Veteran focus group data where data were extracted from transcripts and organized into themes. Results: Various themes emerged from the data including (1) regimented structure, (2) understanding both worlds, (3) congruent recovery journeys, (4) location close enough to services, far enough from harm, and (5) harm reduction is housing stability and housing stability is harm reduction. These themes represent the interface between military culture and homeless culture. Overall, Veterans considered harm reduction as an essential component of housing. Discussion: This study has shown that harm reduction may be part of the solution to ending Veteran homelessness in Canada. The findings of this study may inform how Veteran housing and substance use needs are addressed. Additional research is needed to explore further how harm reduction can be effectively incorporated into Housing First for Veterans.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".