Front-Line Insights Into the Social Determinants of Health in Housing Instability: A Multi-Province Study
Bibliographic record
Abstract
INTRODUCTION: Individuals experiencing housing instability face significant health inequities. Addressing housing instability requires an understanding of the factors that contribute to these inequities-a responsibility that has been assumed by community-based organizations. Interviewing individuals from 3 Canadian provinces, the present study aimed to examine the perspectives of individuals from front-line services of the social determinants of health (SDoH) needs of individuals experiencing housing instability. METHODS: As part of a larger knowledge translation study, we conducted 8 semi-structured interviews with individuals from front-line services (eg, shelter workers and shelter mental health nurses) in Canada (Nova Scotia, Saskatchewan, and Alberta) and used thematic analysis to identify predominant unmet SDoH needs. RESULTS: Individuals from front-line services discussed the roles of many SDoH that may perpetuate housing instability in their clients. These included: (1) limited social supports; (2) poor access to health services; (3) poor opportunities for income and employment; (4) lack of transportation; (5) gender-based discrimination; (6) race-based discrimination; and (7) limited access to education and limited literacy skills. CONCLUSION: This study reveals how front-line service providers observe SDoH factors contributing to housing instability and create barriers to accessing support services. They advocate for a multi-system approach to addressing intersecting SDoH factors to validate clients' experiences and help them achieve stable housing. Additionally, more research and consultation with front-line providers are necessary to understand and overcome systemic barriers to stable housing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".