The National Health Insurance Scheme (NHIS) in the Dormaa Municipality, Ghana: Why Some Residents Remain Uninsured?
Bibliographic record
Abstract
The paper presents a quantitative investigation on the national health insurance scheme (nhis) in dormaa municipality, Ghana: why some residents remain uninsured? Since its implementation has been a little over a decade now. The aim is to identify the obstacles to enrollment by the public which would enable policy direction to ensure that all residents are registered with the scheme. A descriptive and cross-sectional study was conducted between May and July, 2013. Both purposive and simple random sampling technique were used to select 210 respondents and data obtained through self-administered and face-to-face interviews guided by structured questionnaire. chi square (X2) test of independence was adopted to show the association between socioeconomic and demographic features and membership. Findings from the research suggest that residents' decision to enrol have significant associated with gender, education, number of children, place of residence, employment and income. It was also observed that membership is highly affected by premium level. The discussion of the findings and recommendations offered, if incorporated into the policy guideline of NHIS could maintain and at the same time increase enrollment level which would guarantee quality, accessible and affordable basic health care protection for the good people of Ghana.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".