Women in Health and their Economic, Equity and Livelihood statuses during Emergency Preparedness (WHEELER) Protocol: A mixed methods study in Kenya
Bibliographic record
Abstract
Abstract Introduction: Kenya reported its first COVID-19 case on March 13, 2020. Pandemic-driven health system changes followed, as did mitigation measures. These measures had unintended health, economic, and societal consequences: Kenyan women in paid and unpaid employment bore the brunt. This protocol aims to identify potential gender equality and health equity gaps, and possible disproportional health and socio-economic impacts experienced by paid and unpaid female health care providers in Kilifi and Mombasa Counties during the COVID − 19 pandemic. It will also identify evidence-based policy options for future safeguarding of the unpaid and paid female health work force during emergency preparedness, response and recovery periods. Methods: Participatory mixed methods framed by a health equity, gender analysis and human-centred design will be used to engage the unpaid and paid health workforce in the research. Research implementation will follow four of the five phases of the human centred design approach which include, empathize phase, define phase, ideate &synthesis phase, prototype/critical review phase, and testing phase. Data collection in the empathize phase will utilize qualitative (focus group discussions and in-depth interviews) and quantitative (survey questionnaire) to explore perceptions, experiences, needs and priorities of health care providers in relation to COVID-19. This will then be further explored and contextualised in the define phase. In the ideate& synthesis phase, workshops with key stakeholders and health care providers will brainstorm and propose as many gender equitable and transformative recovery solutions as possible for future pandemic preparedness based on the findings from the define phase. In the prototype and critical review phase, the solutions proposed will then be critically appraised and packaged as policy and strategic recommendations that are gender- sensitive and transformative. Community research advisory groups and local advisory boards will be established to ensure integration and sustainability of the participatory research design. Discussion: Globally, seven out of ten health workers are women. This study will generate evidence on root cultural, structural, socio-economic and political factors that perpetuate gender inequities and female disadvantage in the paid and unpaid health sector. Such evidence is critical for the realization of women’s rights, well-being and livelihoods, and for development of gender- sensitive and transformative health systems that can withstand future emergencies and structural shocks.
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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 | 0.038 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".