Data on gender-equitable healthcare accessibility in Northern Nigeria
Bibliographic record
Abstract
Gender equity, particularly in healthcare, has been gaining increasing attention in recent years. The goal is to ensure that everyone has equal access to quality healthcare services irrespective of age, gender, or socio-economic status. However, most countries in sub-Saharan Africa struggle to meet this goal, due to several challenges, including poverty, poor infrastructure, and gender-bias. Using Nigeria as a case-study, it is common knowledge that gender inequality and discrimination is predominant in the northern region of the country. This work sought to gather data to assess the level of healthcare accessibility from a gender-based perspective in northern Nigeria. Data were sourced anonymously from residents in about 500 locations across the northern region of Nigeria, using WhatsApp-based questionnaires, in two phases and two languages - English and Hausa. About 4700 participants took part in the survey and each had to answer 43 questions, split into demographic, socio-economic, wellness check, and diversity, equity, and inclusion (DEI) in health care services obtained.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".