Women in Health and their Economic, Equity and Livelihood statuses during Emergency Preparedness and Response (WHEELER) protocol: a mixed methods study in Kenya
Bibliographic record
Abstract
INTRODUCTION: Kenya reported its first COVID-19 case on 13 March 2020. Pandemic-driven health system changes followed and unforeseen societal, economic and health effects reported. This protocol aims to describe the methods used to identify the gender equality and health equity gaps and possible disproportional health and socioeconomic impacts experienced by paid and unpaid (community health volunteer) female healthcare providers in Kilifi and Mombasa Counties, Kenya during the COVID-19 pandemic. METHODS AND ANALYSIS: Participatory mixed methods framed by gender analysis and human-centred design will be used. Research implementation will follow four of the five phases of the human-centred design approach. Community research advisory groups and local advisory boards will be established to ensure integration and the sustainability of participatory research design. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Institutional Scientific and Ethics Review Committee at the Aga Khan University and the University of Manitoba.This study will generate evidence on root cultural, structural, socioeconomic and political factors that perpetuate gender inequities and female disadvantage in the paid and unpaid health sectors. It will also identify evidence-based policy options for future safeguarding of the unpaid and paid female health workforce during emergency preparedness, response and recovery periods.
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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 | 0.044 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 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".