Gender-related differences in mental health of Canadian Armed Forces members during the COVID-19 pandemic
Bibliographic record
Abstract
LAY SUMMARY The challenges associated with the COVID-19 pandemic have the potential to not only adversely affect mental health in general but also to emphasize and widen disparities in mental health across demographic groups. In particular, research suggests that women have been disproportionately affected by the pandemic psychologically, socially, and economically. However, the state of mental health in the Canadian Armed Forces (CAF) during the pandemic and the impacts of gender on mental health outcomes are currently unknown. This study uses data collected early in the pandemic to examine the state of mental health of CAF Regular Force members and the impacts of gender and family status. Although most members were doing well, a notable minority were experiencing mental health issues at potentially clinically significant levels, with women more likely to experience depression and anxiety than men and women with children less likely to experience functional impairment as a result of their symptoms. The findings provide a snapshot of the mental health of Regular Force members during the pandemic and suggest the importance of considering gender and family situation in understanding mental health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".