Unveiling Women’s Experiences Amidst the COVID-19 Pandemic in Nigeria: An Interpretive phenomenological analysis
Bibliographic record
Abstract
Abstract Background The past three years have seen an increasing gap in health inequalities in Nigeria’s health systems, with many women having trouble accessing health care due to persistent social determinants of health. Studies indicate that the amplified impact of the pandemic is due to the lack of contextual focus on response plans. This study used an interpretive phenomenological analysis to analyze women’s experiences with healthcare as the pandemic progressed in Nigeria. Methods Semi-structured interviews were conducted between August to November 2022. It was supplemented with three focus group discussions with mothers. The mothers were purposively recruited for their experiences with health care during the pandemic. The analysis of the interviews followed the interpretive phenomenological analysis approach. Results Twenty-four women aged 15 to 49 years with children between 0 and 5 years participated in this study. These women reported mixed experiences during the pandemic, with many attributing positive health behaviours to the pandemic. Following analysis, four themes emerged: i) Influence of diversity of Healthcare Practices and Beliefs on health-seeking behaviour; ii) Unpacking Systemic Barriers to Seeking Timely and Appropriate Health Care Services; iii) Women’s fear of contracting COVID-19; iv) Socioeconomic Burden for Holistic Health Care Delivery. Conclusion Health planners must examine contextual factors that drive health usage, especially potentially changing gender dynamics ahead of the next pandemic. This paper examined women’s decision to seek or not seek care, the type of care they received, and where they went for care. Women felt that the pandemic affected their decision to seek or not seek care. However, while they learned new behaviours that are now integrated into their daily lives, they also indicate that some behaviours are habitual and have persisted through the pandemic.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".