BEING AND STAYING ELIGIBLE FOR SERVICES: PROVIDERS’ VIEWS ON SUPPORTING OLDER ADULTS EXPERIENCING HOMELESSNESS
Bibliographic record
Abstract
Abstract Older people experiencing homelessness (OPEH) are a marginalized population with limited access to shelter/housing supports. Amidst these limited resources, understanding program-specific (in)eligibility criteria, along with strategies to sustain tenancy in different shelter/housing settings, is crucial to supporting OPEH in their housing goals. This study examined providers’ understanding of housing (in)eligibility in different shelter/housing settings, factors that impact the criteria for staying eligible, and interventions and steps providers use in assisting OPEH to maintain their eligibility and move towards their housing goals. Semi-structured interviews were conducted with 15 providers from five shelter/housing organizations serving OPEH across three Canadian cities. These organizations represented a range of shelter/housing services for OPEH, including emergency shelters, temporary/transitional housing, and permanent supportive housing. We conducted a thematic analysis and organized findings into three themes: 1) Medical and behavioral factors that impact (in)eligibility of OPEH for shelter/housing services; 2) Challenges to maintaining eligibility due to medical and behavioral factors; and 3) Provider and organizational support mechanisms to sustain tenancy. Findings provide insight into who is (in)eligible for different shelter/housing programs catered to OPEH, along with different support mechanisms available to support OPEH in their housing goals. Findings highlight the supports and services needed by OPEH with medical and behavioral challenges to remain eligible in different shelter/housing settings and recommendations for how to increase and target services to OPEH with specific needs. Understanding medical and behavioral factors that impact the shelter/housing tenancy of OPEH provides an opportunity to foster client success and promote positive housing outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".