Impact of the COVID-19 pandemic on women in the workplace in the Middle East and North Africa: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has disrupted the livelihoods of working men and women worldwide. The pandemic exacerbated already existing inequities, especially in sectors where women predominate, such as the healthcare, education, and hospitality sectors. Women in the Middle East and North Africa (MENA) region, a world outlier for low female labor force participation despite high female education levels, may have been disproportionately impacted by the pandemic. Understanding the impact of COVID-19 on women's livelihoods and health is critical to support and retain women in the labor force during external health shocks. However, until now there has been relatively little research on this topic in MENA. Therefore, this scoping review aims to determine the impact of the COVID-19 pandemic on the health and wellbeing of women workers in low-and-middle-income countries in the MENA region. METHODS: The outcome of interest is COVID-19 related occupational health (COVID-19 infection related to workplace exposures and pandemic-related occupational stressors) and the impact on mental and physical health among women workers in LMIC in MENA. Academic databases, including APA PsycINFO, Arab World Research Source: Al-Masdar, Global Health, MEDLINE, Scopus and Web of Science Core Collection will be searched. The study selection process will involve two independent reviewers and data extraction will involve summarizing key information from the included studies using a predefined charting table. The evidence will be analyzed descriptively, providing a comprehensive overview of the identified themes and patterns. DISCUSSION: It is anticipated that this review will facilitate a deeper understanding of the impact of the COVID-19 pandemic on working women in the MENA region. The findings may inform data-driven policies and targeted interventions that not only attract and retain women in the workforce but also enhance their health and well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".