Do neighbourhood challenges affect the mental health of residents? Insights from the 2018 and 2021 Canadian Housing Surveys
Bibliographic record
Abstract
Abstract Neighbourhood physical and social disorders are shown to have adverse impacts on residents’ mental health. Identifying and addressing neighbourhood challenges is crucial for promoting social cohesion and mental well‐being. Nonetheless, there is a dearth of research on this important topic within Canada, prompting a comprehensive evaluation of the association between neighbourhoods’ challenges and self‐rated mental health. Using the 2018 (N = 61,021) and 2021(N = 40,988) Canadian Housing Surveys and employing logistic regression models for comparative analysis, we found that residents of neighbourhoods with challenges including harassment, drug use, drunkenness, unsafeness at night, noise, smog/air pollution, garbage litter, and vandalism, reported lower odds of positive mental health (PMH) both pre‐pandemic and during the COVID‐19 pandemic. Also, females reported lower odds of PMH both pre‐pandemic and during the pandemic. However, residents with post‐secondary educational attainment, those from wealthy households, and those in two‐member households, significantly reported PMH before and during the pandemic. Civic engagement with the local community also correlated more with PMH, but pre‐pandemic only. Provincial variations were further observed. Thus, we concluded that neighbourhood challenges contribute to poor mental health. Socio‐economic and provincial differences underscore the importance of tailored interventions and support systems for mental health across regions. However, it is important to highlight that the self‐reported nature of our data may result in biased perceptions. That is, participants’ existing poorer mental health status may influence their opinions about the neighbourhoods. Also, the tendency of social desirability to influence responses may suggest a bidirectional neighbourhood‐mental health relationship.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".