Exploring Women’s Mental Health During a Pandemic
Bibliographic record
Abstract
Purpose: Socio-demographic inequities in mental health were magnified by COVID-19, with women experiencing greater household burden with less support in Canada and globally. While se patterns have been observed globally, there is a research gap in rural mental health during COVID-19 in Canada. We hypothesize there is a disparity in mental health decline during COVID-19 between men and women. Methods: In rural Ontario, mental health was measured through a survey of approximately 18,000 individuals living in seven counties. In 2021, survey respondents were asked to rate their mental health prior to and during COVID-19. Women reported poorer mental health during COVID-19 in comparison to men when tested via chi-squared, odds ratios, and percent change. Responses to survey questions regarding, social, financial, and mental health support were then evaluated. Findings: We found significant disparities in mental health ratings before and during COVID-19 between men and women. Women reported poorer mental health, increased substance use, and increased worry about social, financial and community stressors. Respondents who self-identified as a woman were associated with poorer mental health outcomes. Conclusions: Interventions should be specific to geographic communities as well as individual needs (e.g., additional financial and childcare support). Rural communities need to be considered as independent geographies, rather than as one geography (i.e. urban vs. rural).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".