Insights into gender-equity in healthcare accessibility in Northern Nigeria: descriptive and predictive approaches
Bibliographic record
Abstract
Universal Health Coverage (UHC) aims at ensuring equitable access to healthcare for everyone, irrespective of gender, location, or financial status. Though progress has been made in achieving UHC, a lot remains to be done in under-served areas of the world. These regions face immense challenges accessing healthcare services, including unavailability of basic medications, socio-cultural and religious beliefs, and various forms of discrimination. Beyond these, women are still severely disadvantaged in these regions, with child brides and teenage pregnancy being prevalent. This work analysed data from regions of northern Nigeria to determine equity in healthcare accessibility. Descriptive analysis (using correlation models) and predictive analysis (using machine learning models) were carried out. The descriptive analysis revealed that women with low income and education levels, and the elderly have a higher chance of accessing healthcare services compared to other genders, while the predictive analysis revealed that, using machine learning, accessibility to healthcare services can be predicted with up to 81% accuracy.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".