Impact of the COVID-19 pandemic on intra-household gender disparities in the Middle East and North Africa region: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The Middle East and North Africa (MENA) is a global outlier both for its low female labor force participation and investment in early childhood development services, and consistently ranks lowest on global rankings of gender parity. While the impact of the COVID-19 pandemic on global gender inequity has been acknowledged, specific challenges faced by partnered-women in MENA are underexplored. Additionally, with over half of the region affected by conflict and displacement, exploring these impacts sheds light on understudied gender disparities in humanitarian contexts during the pandemic. This scoping review aims to examine intra-household gender disparities during the COVID-19 pandemic, adding to a more comprehensive understanding of this particular public health emergency's worldwide ramifications. The hypothesis is that the pandemic has exacerbated women's pre-existing constraints in the MENA region, worsening gender inequities in economic empowerment, healthcare access, and general well-being. METHODS: This review will explore intra-household gender disparities in decision-making, household labor division, economic independence, health, and access to resources and services during the COVID-19 pandemic that have been reported to have been affected by COVID-19 globally. Following PRISMA guidelines for Scoping Reviews, a comprehensive search will be conducted in APA PsycINFO, Arab World Research Source: Al Masdar, EconLit, Global Health, MEDLINE, Scopus, and Web of Science Core Collection, in consultation with an information specialist. Studies in English, French and Arabic from January 2020 to August 2024 will be included. Four independent reviewers will screen studies, and data will be charted, coded, and narratively synthesized. DISCUSSION: This review is expected to shed light on gender disparities in healthcare accessibility, mobility, and decision-making during the COVID-19 pandemic across low- and middle-income countries in the MENA region, adding to the global literature.
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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 | 0.080 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.011 |
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".