Gendered Inequalities within Ghana’s National Health Insurance Scheme: Are Poor Women Being Penalized with a Late Renewal Policy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article addresses the implications of the mandatory delay in coverage for individuals residing in the Upper West Region (UWR) of Ghana who have dropped out of the National Health Insurance Scheme (NHIS) but later attempt to reenroll. Using data collected in 2011 in Ghana's UWR, we use a negative log-log model (n=1,584) to compare those who remain enrolled in the scheme with those who have dropped out. Women with unreliable incomes, who reported being food-insecure and those living with young children were more likely to drop out (OR range: 1.22-1.79, p<.05). Men, in contrast, were 50% more likely to drop out of the NHIS for being unsatisfied with services provided (OR range: 1.25-1.62, p<.01). Contrary to the original mandate of the NHIS, our study reveals clear gender differences in the factors contributing to dropouts, pointing to a bias in the impact of the block-out policy that is penalizing women for being poor.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it