Challenges of Implementing Tertiary Institution Social Health Insurance Programme: Empirical Evidence From Southwest Nigeria
Bibliographic record
Abstract
Managed care is still prematurely implemented in tertiary institutions in Nigeria. This study then examined the challenges of implementing Tertiary Institution Social Health Insurance Programme (TISHIP) in federal universities in southwest Nigeria between 2005 and 2019. Primary and secondary data were used for this study, while University of Ibadan (U.I.) and Obafemi Awolowo University (O.A.U.) were purposively selected from the study population. The study concluded that the following challenges confront the implementation of TISHIP in southwest Nigeria, and these include: irregular feedback in the implementation of TISHIP (RII=3.52), failure to educate students about the benefits of implementing TISHIP (RII=3.50), lack of public advocacy to generate support for the objectives of TISHIP (RII=3.45), lack of transparency in the collection and remmitance of the sickness fund (RII=3.01), and poorly constituted TISHIP management committee. The recommendations of the study suggest a robust funding for implementing TISHIP to enhance the capacity of the Scheme to provide accessible and affordable health care services for students of federal universities in southwest Nigeria.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".