Health insurance enrolment in the Upper West Region of Ghana: Does food security matter?
Bibliographic record
Abstract
Toward achieving universal health coverage, Ghana's national health insurance has been acclaimed as a pro-poor scheme, yet been criticized for leaving the poor behind. Arising from this is how poverty has been operationalized and how poor people are targeted for enrolment into the scheme. We examine the role of food insecurity (not currently considered) as a multidimensional vulnerability concept on enrolment into Ghana's health insurance using binary logistics regression on cross-sectional survey of household heads (n = 1438) in the Upper West Region of Ghana. Our analyses show that heads of severely food-insecure households were significantly less likely to enroll in national health insurance scheme (NHIS) relative to households who reported being food-secure (OR = 0.36, P < .05). We also found education, occupation, and religion as significant predictors of health insurance enrolment. Based on our findings, it is crucial to incorporate food security status in the identification of vulnerable people for free enrolment in Ghana's health insurance.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".