Comparisons of depression, anxiety, well-being, and perceptions of the built environment amongst adults seeking social, intermediate and market-rent accommodation in the former London Olympic Athletes’ Village
Bibliographic record
Abstract
The Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) study provides a unique opportunity to examine differences in mental health and well-being amongst adults seeking social, intermediate (affordable rent), and market-rent housing in a purpose built neighbourhood (East Village, the former London 2012 Olympic Athletes' Village), specifically designed to encourage positive health behaviours. Multi-level logistic regression models examined baseline differences in levels of depression, anxiety and well-being across the housing groups. Compared with the intermediate group, those seeking social housing were more likely to be depressed, anxious and had poorer well-being after adjustment for demographic and health status variables. Further adjustments for neighbourhood perceptions suggest that compared with the intermediate group, perceived neighbourhood characteristics may be an important determinant of depression amongst those seeking social housing, and lower levels of happiness the previous day amongst those seeking market-rent housing. These findings add to the extensive literature on inequalities in health, and provide a strong basis for future longitudinal work that will examine change in depression, anxiety and well-being after moving into East Village, where those seeking social housing potentially have the most to gain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".