Addressing the Stigma of Homelessness Through Employment and Community Education: Experiences of Participating in Two Novel Initiatives in Kingston, Ontario, Canada
Bibliographic record
Abstract
Stigma is a serious issue affecting the lives of persons who experience homelessness and use substances. It influences every aspect of their daily lives including where they exist in public places, where they can live following homelessness, and access to housing, employment, and other opportunities that would enable thriving. In this report, we present the findings of two distinct studies which explore the experiences of administering or participating in two initiatives aimed at mitigating the stigma of homelessness in the lives of individuals who experience homelessness and use substances in Kingston, Ontario, Canada. These two initiatives were designed and led by Trellis HIV and Community Care in collaboration with a range of community agencies throughout the Kingston community and included: 1) the “Vocational Program,” an initiative aimed at providing accessible, low-barrier employment to individuals who are currently unhoused and engaged in active substance use; and 2) the “Support Not Stigma” workshop series, a series of seven workshops aimed at reducing stigma among service providers in services for individuals who experience homelessness and use substances. While this research was qualitative and not aimed at measuring the effectiveness of these interventions, our findings indicate that these two approaches were worthwhile initiatives that need to be implemented and evaluated in future implementation efforts. We provide several recommendations for research, policy and practice for consideration by researchers, policymakers and service providers in future efforts designed to reduce stigma in services and the broad Kingston community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| 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".