Will private health insurance schemes subscriptions continue after the\nintroduction of National Health Insurance in Uganda?
Bibliographic record
Abstract
Introduction: Uganda is currently designing a National Health Insurance (NHI) scheme, with the aim of raising additional resources for the health sector.Very little was known about the health insurance market in Uganda before this study, so one of our main objectives was to investigate the nature of the private health insurance market in Uganda and the opinions of various stakeholders on NHI, with the view to establish the impact of NHI implementation on the existing PHI.Specifically, we aimed to gather the opinions of employees and employers on the likely impact of NHI on their PHI schemes.Methods: We conducted interviews with health insurance providers, and a sample of employers and employees in Kampala, using structured questionnaires and analysed quantitative data using STATA 8 .Qualitative data was analysed through grouping of emerging themes.Community-based health insurances were excluded from the study.Results: Health insurance and/or prepayment schemes are offered by a handful of organisations or private health providers, mainly in Kampala and cover a relatively small percentage of Uganda's population.The premiums charged and the benefit packages offered by the different agencies vary widely.There are 2 health insurance agencies, 2 HMOs and about 5 or more private providers offering pre-payment schemes to their patients.Responses from a significant proportion of employers and employees show that PHI schemes may be abandoned once the mandatory NHI scheme is implemented.A few respondents argued that they would maintain their PHI subscriptions because of their perceptions of the quality of services likely to be provided under the NHI scheme.Conclusion: If successfully introduced, the NHI scheme may displace existing private health insurance and/or pre-payment schemes in Uganda.The extent to which PHI schemes are displaced depends on whether NHI is successfully implemented and the quality of services being offered under the NHI scheme.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".