Understanding the Gendered Impact of COVID-19 on Young Self-Employed Nigerian Women and Coproducing Interventions That Foster Better Systems and Well-Being: Protocol for a Multimethods Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had disproportionate economic and health impacts on self-employed workers in Nigeria, particularly self-employed women and youth. Though uniquely different, the COVID-19 pandemic shares similarities with events such as childbirth, family, and health emergencies. Self-employed young women lack adequate support structures to cope with disruptive life events, which have negative consequences for their well-being. This is concerning, as 86% of women in the Nigerian labor force are self-employed. OBJECTIVE: The project's first objective is to conduct a gendered situational analysis to address the question of how the COVID-19 pandemic and other life events affect the paid and unpaid work and the physical, mental, and social well-being of self-employed young women in Nigeria; their strategies for coping with such events; and how their experiences compare with those of self-employed young men. Informed by this analysis, the second objective is to coproduce and pilot-test a gender-transformative intervention that integrates social protection and promotes well-being. METHODS: This multimethod project has 3 components. The first is a situational analysis of the impact of the pandemic and other significant life events on the work and well-being of self-employed young women vis-à-vis self-employed young men. This involves qualitative interviews with approximately 60 self-employed young women and men and a digital storytelling initiative to represent some of these stories in video format. Secondary data analysis of the Nigerian General Household Survey and the COVID-19 Longitudinal Phone Survey will be conducted. Furthermore, a scoping review of the impact of significant life events, including the COVID-19 pandemic, on self-employed workers in low- and middle-income countries will be conducted. The second component is the coproduction of interventions involving qualitative interviews with self-employed young women, members of their support network, and policy makers to find out their views on how to support self-employed women. It also entails an analysis of policies relevant to self-employed women in Nigeria and theory of change workshops to create a map for achieving the long-term goal of improving their resilience. Furthermore, a systematic review of interventions to improve the job quality and well-being of self-employed workers will be conducted. The third component is a pilot of the coproduced interventions in a quasi-experimental study involving 300 participants to assess feasibility, acceptability, cost, and potential effectiveness. RESULTS: This project was funded in October 2022. Data collection for the project commenced in May 2023 and will end in November 2025. Data collection for the situational analysis and coproduction of intervention phases have been completed while the pilot of intervention packages is underway. CONCLUSIONS: This project will advance knowledge of the impact of the COVID-19 pandemic and other significant disruptive life events on the work and well-being of self-employed young Nigerian women and provide coproduced solutions to mitigate their effects. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69577.
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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 | 0.032 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.008 |
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".