Mental health and residential adaptations during the pandemic: a mixed-methods study of working mothers
Bibliographic record
Abstract
In the Spring of 2020, the COVID-19 pandemic transformed dwellings worldwide into focal points of daily life. This mixed-methods study examines how women in various residential situations adapted when faced with pandemic challenges and how their adaptations influenced the meanings of home and mental health. A pool of 538 working mothers was identified from Quebec’s MAVIPAN repeated measures survey to test if their residential situations, marked by their multiple social roles as mothers, workers, wives, or teachers, and various housing conditions, were associated with mental health and their meanings of home. Four profiles of residential situations were identified through multiple factor analysis (MFA), and 33 women belonging to these profiles were interviewed. Quantitative explorations and ChatGPT-4-assisted thematic analysis revealed conditional associations linking vulnerable residential situations to adaptation difficulties, low mental health scores, and negative feelings toward home, applying to women in blended or single-parent families, in small dwellings, or self-employed.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".