Damp housing conditions as a determinant of psychological distress: a longitudinal analysis of the British Household Panel Survey
Bibliographic record
Abstract
Limited evidence exists regarding whether damp housing contributes to psychological distress. This study aimed to quantify the relationship between damp housing exposure and psychological distress. Data from the British Household Panel Survey (1996-2008) were used to assess the effect of damp housing on psychological distress in British households (n = 9189 at baseline). Indoor dampness exposure was measured using multiple indicators (condensation, leaky roof, rot, and damp walls/floors) and a measure of severity that quantified the number of exposures. Psychological distress was measured using a binary variable derived from the General Health Questionnaire. Multivariate fixed effects logistic regression models analyzed the hypothesized associations. Exposure to damp housing was associated with increased odds of psychological distress (OR, 1.09; 95% CI, 1.05-1.14; P < .01). Condensation was the strongest predictor (OR, 1.09; 95% CI, 1.03-1.13; P < .01). With each additional dampness indicator, odds of psychological distress increased by 4% (OR, 1.04; 95% CI, 1.02-1.07; P < .01). Among combinations of dampness indicators, the strongest association was for condensation and rot in windows/floors (OR, 1.25; 95% CI, 1.11-1.40; P < .01). These findings suggest damp housing exposure may increase the risk of psychological distress. Further research should investigate underlying mechanisms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".