“We could just be what we wanted to be”: The role of leisure and recreation in supporting women’s mental health during COVID-19
Bibliographic record
Abstract
Women’s mental health has been disproportionately impacted by the COVID-19 pandemic. Women have experienced higher rates of unemployment, domestic violence, caregiving responsibilities and reduced access to social supports because of public health measures related to COVID-19. It is well established that leisure and recreation can support mental health, yet, the role of leisure and recreation in supporting women’s mental health during COVID-19 is relatively unknown. In partnership with the Canadian Mental Health Association-Yukon, the purpose of this community-based participatory research study was to understand how leisure and recreation might support women’s mental health in Whitehorse, Yukon Territory during COVID-19. Twelve self-identifying women between the ages of 22–65 years participated in one-on-one semi-structured interviews. A participatory data analysis approach was employed and the findings are represented by five themes: (a) focus on yourself, (b) facilitating feel-good emotions, (c) connection and support networks, (d) navigating the northern context, and (e) women-identified opportunities. Findings suggest leisure and recreation offer various processes that assist women with managing stressful situations that in turn support their mental health. These processes include promoting self-determination, generating positive emotions, and strengthening connectedness. Actionable steps to support women’s mental health in a northern context are also presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".