“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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".