“It’s what we perceive as different”: an interpretative phenomenological analysis of Nigerian women’s characterization of their health during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Health has historically been adversely affected by social, economic, and political pandemics. In parallel with the spread of diseases, so do the risks of comorbidity and death associated with their consequences. As a result of the current pandemic, shifting resources and services in resource-poor settings without adequate preparation has intensified negative consequences, which global service interruptions have exacerbated. Pregnant women are especially vulnerable during infectious disease outbreaks, and the current pandemic has significantly impacted them. METHODS: This study used an interpretive phenomenological analysis study with a feminist lens to investigate how women obtained healthcare in Ebonyi, Ogun, and Sokoto states Nigeria during the COVID-19 pandemic. We specifically investigated whether the epidemic influenced women's decisions to seek or avoid healthcare and whether their experiences differed from those outside of it. RESULTS: We identified three superordinate themes: (1) the adoption of new personal health behaviour in response to the pandemic; (2) the pandemic as a temporal equalizer for marginalized individuals; (3) the impacts of the COVID-19 pandemic on maternal health care. In Nigeria, pregnant women were affected in a variety of ways by the COVID-19 epidemic. Women, particularly those socially identified as disabled, had to cross norms of disadvantage and discrimination to seek healthcare because of the pandemic's impact on prescribed healthcare practices, the healthcare system, and the everyday landscapes defined by norms of disadvantage and discrimination. CONCLUSION: It is clear from the current pandemic that stakeholders must begin to strategize and develop plans to limit the effects of future pandemics on maternal healthcare, particularly for low-income women.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".