Exploring the bounds of consumer choice in supported housing: A reflexive thematic analysis of data generated from supportive housing tenants in British Columbia, Canada
Bibliographic record
Abstract
Consumer choice is a key principle in Housing First and supported housing models. Through the provision of permanent housing and individualized supports, these models promote an empowerment and recovery-oriented approach that advances individual self-determination and overall wellbeing. The purpose of this study is to explore housing tenants' experiences, with, and perspectives of, complex care and supportive housing models in British Columbia (BC), Canada to understand how well these models align conceptually with the notion of consumer choice. Viewed through a critical theoretical framework and applying a reflexive thematic analysis to interview data generated from housing tenants in BC, we constructed several themes that spoke directly or indirectly to dimensions of consumer choice. The essence of 'constrained choice' in these data suggest that the underlying components of consumer choice may be infrequently practiced in complex care and supportive housing programs in BC. Given the importance of consumer choice in improving the quality of life and wellbeing of supported housing tenants, work is necessary to broaden its scope and exercise in these contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".