Medical Negligence and the Nigerian National Health Insurance Scheme: Civil Liability, No-Fault, or a Hybrid Model?
Bibliographic record
Abstract
Academic debates on the most expedient method for addressing medical malpractice veer between reform of the tort of medical negligence or adoption of the no-fault compensation scheme. The no-fault system, which is operative in countries such as New Zealand and Sweden, has become the other alternative against which the tort system is measured. This paper examines these two systems while evaluating the parameters outlined by the Nigerian National Health Insurance Scheme to address medical error. It assesses whether the no-fault system or similar administrative frameworks can cater to the demands of medical accountability in Nigeria. It appraises the objectives of the tort system, tort being one of the regulatory approaches adopted by the Nigerian Scheme, and questions the critique of this system through the no-fault system, a system constructed on an entirely different agenda. The author suggests that proposals for malpractice regulation should be contextually-situated and multifaceted, as this debate is not one that should be argued outside the socio-economic and socio-demographic actualities of a healthcare state.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| 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".