Framework and Enforcement Strategy for Health Professions Regulation in Ethiopia
Bibliographic record
Abstract
This thesis examines the best system for health professions regulation in Ethiopia with a view to sketch the roles of state and non-state actors in that system. It argues for statist regulation as self-regulation is worrisome for its tendency to promote private interest instead over public protection. A statist regulation is an efficient system that is more capable of establishing accountable and procedurally fair processes and strengthening public trust than a system of self-regulation. But the state lacks capacity, expertise, and legitimacy, and risks capture and corruption. These could be resolved through an enforcement strategy rooted in responsive regulation theory. That strategy should emphasize soft regulatory instruments, which requires utilization of the capacity and motivation of non-state actors, particularly health professional associations. A statist regulatory framework that harnesses the contribution of non-state actors in implementing soft regulatory strategies would effectively protect patients and improve the quality of health care services in Ethiopia.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".