Women’s Healthcare Accessibility and Utilisation Post-COVID-19 in the Niger-Delta Region of Nigeria
Bibliographic record
Abstract
Worldwide, the healthcare system has witnessed significant changes in accessibility and utilization during and after the COVID-19 pandemic. There has been a reported marked reduction observed globally in healthcare utilization, especially after the pandemic. This research scrutinized the influence of several determinants of healthcare accessibility and utilization after COVID-19. The data was quantitatively analysed from a household survey of 2423 female rural participants from Delta and Edo States in the Niger-Delta Region of Nigeria. SPSS 23 software was used for the results examined, while Arc GIS 10 Software was used for Map production. Descriptive and multiple regression techniques were used to analyse the data. The research findings depicted the change in healthcare utilization from primary healthcare to hospital due to challenges in the quality of healthcare in rural areas. The region is characterized by (84% of) women who have low or no income and are either farmers or traders. Healthcare services utilized include maternal and child care (pre-natal, delivery, family planning) among the research participants. Twenty-four variables explained rural women’s healthcare accessibility and utilization at 77% which is statistically significant. This research recommends the improvement in women’s productivity and livelihood, change in social infrastructure to boost equality between the genders, combating the social and economic consequences after COVID-19 and integration of active community and government participation in the healthcare system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".