Did the UK COVID-19 Lockdown Modify the Influence of Neighbourhood Disorder on Psychological Distress? Evidence From a Prospective Cohort Study
Bibliographic record
Abstract
Background: National lockdown in the UK during the COVID-19 pandemic severely restricted the mobility of residents and increased time spent in their residential neighbourhoods. This is a unique opportunity to understand how an exogenous factor that reduces mobility may influence the association between neighbourhood social environment and mental health. This study investigates whether the COVID-19 lockdown may modify the effect of neighbourhood disorder on psychological distress. Methods: We tracked changes in psychological distress, using the UK household longitudinal survey across the pre-COVID and lockdown periods in 16,535 adults. Neighbourhood disorder was measured along two subscales: social stressors and property crime. Fixed-effects regression was used to evaluate whether the widespread reduction in mobility modifies the association between the subscales of neighbourhood disorder and psychological distress. Results: The effect of neighbourhood social stressors on psychological distress was stronger in the lockdown period compared to the pre-COVID period. Compared to the pre-COVID period, the effect of being in neighbourhoods with the highest social stressors (compared to the lowest) on psychological distress increased by 20% during the lockdown. Meanwhile, the effect of neighbourhood property crime on mental health did not change during the lockdown. Conclusion: The sudden loss of mobility as a result of COVID-19 lockdown is a unique opportunity to address the endogeneity problem as it relates to mobility and locational preferences in the study of neighbourhood effects on health. Vulnerable groups who have limited mobility are likely more sensitive to neighbourhood social stressors compared to the general population.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".