Risk management and insurance failures: the case of Ghana’s National Health Insurance Scheme
Bibliographic record
Abstract
Since its inception, Ghana’s National Health Insurance Scheme (NHIS) has brought about notable improvements in healthcare access and health outcomes. However, challenges have arisen, threatening its intended function. This study aims to investigate the challenges encountered by NHIS participants and their risk management practices in response to insurance failures. Convenience and purposeful sampling techniques were employed to gather data from 45 Quality Insurance Company (QIC) employees residing in the Greater Accra area of Ghana who are NHIS members. A quantitative approach was utilized, and a questionnaire was administered to collect data. The study employed Cronbach’s alpha to assess the reliability and validity of the data. Additionally, Regression analysis, correlation, and descriptive statistics were utilized to achieve the study's objectives. The study revealed several challenges facing the NHIS, including a shortage of medical specialists, medications, and hospital beds, as well as high patient out-of-pocket expenses. Despite these challenges, the NHIS has not yet failed. However, the majority of NHIS cardholders (62.20%) would be adversely affected in the event of system failure, as they primarily rely on NHIS for risk management. Conversely, a minority of participants (37.80%) are less likely to experience the effects of failure due to alternative insurance plans and personal savings. Therefore, understanding these dynamics is crucial for policymakers and stakeholders to strengthen the NHIS and ensure sustainable healthcare access for all Ghanaians.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".