Beyond Securing a tenancy: using the capabilities approach to identify the daily living needs of individuals during and following homelessness
Bibliographic record
Abstract
Individuals experiencing homelessness have a range of unmet needs during homelessness. Less is known about how these daily living needs evolve in the transition to housing and how existing services are supporting individuals to meet these needs. Using the capabilities approach, we conducted a cross-sectional qualitative study with two participant groups (unhoused; housed between 1 and 24 months). Using thematic analysis, we generated five meta-themes to describe the experiences and compare these two groups: (1) Managing mental health and substance use; (2) Creating connection and community; (3) Involvement in something meaningful; (4) Emotional adaptation to impoverished living conditions; and (5) The helping relationship as foundation. Our findings indicate that for individuals to thrive following homelessness, researchers, service providers, and policymakers need to design and implement strategies that target outcomes beyond tenancy sustainment. These key strategies include: (1) facilitating access to resources that enable thriving following homelessness; (2) developing measures that orient programs and policies to target thriving rather than sustaining a tenancy; and (3) including persons with lived experiences of homelessness in the design and delivery of services and in the evaluation of new and existing programs.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".