Exploring the impact of gender on challenges and coping during the COVID-19 pandemic
Bibliographic record
Abstract
Purpose Repercussions to everyday life caused by the COVID-19 pandemic disproportionately impacted certain segments of the population, including older adults, communities of color and women. The societal response to reduce the impact of the pandemic, including closing schools and working from home, has been experienced differentially by women. This study explored how individual challenges and coping mechanisms differed for women as compared to men. Design/methodology/approach This study used an anonymous, cross-sectional, online survey early in the COVID-19 pandemic. Convenience, snowball and purposive sampling methods were used. Data were collected in June 2020 targeting adults living in Canada and the USA, with a total of 1,405 people responding, of which, the respondents were primarily women, White and with high education levels. Findings The results of this study confirm previous research that women struggled more to adapt to the pandemic and felt less prepared than men during the COVID-19 pandemic. Further, this study found significant differences in the sources of information and support used by women as compared to men. Originality/value The findings of this study not only confirm past research but also highlight that practice and policy responses to this pandemic, and future research on national level crises need to be targeted by gender, so that different needs are effectively addressed. Additionally, this article also identifies sources or challenges, as well as support, in order to inform and strengthen such responses.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".