And Still She Rises: Policies for Improving Women’s Health for a More Equitable Post-Pandemic World
Bibliographic record
Abstract
The COVID-19 pandemic has spawned crises of violence, hunger and impoverishment. Maternal and Infant Health Canada (MIHCan) conducted this policy action study to explore how changes that have arisen during the COVID-19 pandemic may catalyze potential improvements in global women's health toward the creation of a more equitable post-pandemic world. In this mixed methods study, 280 experts in women's health responded to our survey and 65 subsequently participated in focus groups, including professionals from India, Egypt/Sudan, Canada and the United States/Mexico. From the results of this study, our recommendations include augmenting mental health through more open dialogue, valuing and compensating those working on the frontlines through living wages, paid sick leave and enhanced benefits and expanding digital technology that facilitates flexible work locations, thereby freeing time for improving the wellbeing of caregivers and families and offering telemedicine and telecounseling, which delivers greater access to care. We also recommend bridging the digital divide through the widespread provision of reliable and affordable internet services and digital literacy training. These policy recommendations for employers, governments and health authorities aim to improve mental and physical wellbeing and working conditions, while leveraging the potential of digital technology for healthcare provision for those who identify as women, knowing that others will benefit. MIHCan took action on the recommendation to improve mental health through open conversation by facilitating campaigns in all study regions. Despite the devastation of the pandemic on global women's health, implementing these changes could yield improvements for years to come.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".