The contribution of residential situations to mental health during COVID-19: A longitudinal survey
Bibliographic record
Abstract
During the early stages of the COVID-19 pandemic, extended periods of confinement made housing the focal point of daily life. While the impact of sanitary health measures on mental health has been extensively studied, the role of housing remains less understood. Our literature review indicates that housing characteristics and usage patterns influenced mental health outcomes during the pandemic. This study aims to test the hypothesis that the concept of residential situation –an original framework integrating housing attributes, individual and household characteristics, and occupational profiles– is associated with stress, wellbeing, depression, and anxiety measures. We employed data mining techniques and ordinal logistic regression models on a sample of 781 participants from a longitudinal survey conducted in Québec, Canada, between April 2020 and May 2021. Our findings reveal that higher dwelling occupation density is positively associated with increased stress levels. Additionally, apartment living, after adjusting for the number of children under the age of nine, shows a significant association with stress. Feelings of depression and low wellbeing are linked with experiencing separation from loved ones. Also, depression, wellbeing, and anxiety measures were found to be strongly associated with income. This study underscores the effectiveness of a comprehensive framework, applying the interdisciplinary concept of residential situations to capture the nuanced impact of housing on mental health through its links to family dynamics, and shows the importance of social class membership for mental health during crises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".