“Making a homeless person even more homeless … it’s a necessary evil”: The health and safety complexities of service restrictions as perceived by emergency shelter staff.
Bibliographic record
Abstract
Service restrictions involve people experiencing homelessness being banned from an emergency shelter in response to a violation of program policies. Their use can be a pathway into unsheltered homelessness and reliance on other institutional services. However, the prevalence of service restriction use and the perspectives of shelter staff toward the practice are unknown. Accordingly, this mixed methods study used a one-phase QUAL(quan) embedded design to examine the reasons for implementing service restrictions, as recorded by shelter staff in administrative data and their perceptions of the practice, with a focus on impacts. Two sources of data were used: (a) semistructured interviews with 30 staff working in shelters in Toronto, Canada, and (b) administrative data from 2014 to 2021 on the reasons for service restriction issuance across Toronto's shelter system. Assault and threatening or violent behavior were common reasons for service restriction and had increased in recent years. Despite the prevalence of their use, service restrictions were perceived as a necessity ("necessary evil") by shelter staff but could also challenge and unsettle staff due to the potential for further harms to people experiencing homelessness ("making a homeless person even more homeless"). Improving violence prevention approaches, developing alternative responses for nonviolent behaviors that violate shelter policies, and implementing training and support interventions to enhance workplace mental health and wellness among shelter staff could address underlying issues linked to service restrictions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".