Exploring the Connection between Social Housing and Employment: A Scoping Review
Bibliographic record
Abstract
Adequate housing is a social determinant of health and well-being, providing stability from which people can engage in important life activities, including self-care and productivity. Social housing is a system-level intervention that aims to provide affordable housing to people vulnerable to experiencing social and economic marginalisation. Given the importance of employment to social-economic status and overall health and well-being, we sought to better understand the available knowledge and research related to employment and living in a social housing environment. We used scoping review methodology to explore peer-reviewed research published between 2012-2022 regarding social housing and employment, identifying 29 relevant articles. Using the Psychology of Working Theory and neighbourhood effects as interpretive theoretical frameworks, we analysed the extracted data. Overall, the results affirmed that social housing residents have low employment rates conceptualised as related to the complex interplay of a range of personal and environmental factors. Most published literature was quantitative and originated from the United States. Policy and research implications are discussed, including the need for more multifaceted, person-centred interventions that support employment and ultimately promote health and quality of life for social housing residents.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".