Introducing a National Licensing Examination: the case of Ethiopian Associate Clinician Anesthetists
Bibliographic record
Abstract
Ethiopia drastically increased the anesthesia workforce density by training associate clinician anesthetists (also referred to as anesthetists) as a task-shifting and sharing strategy. However, there were growing concerns about educational quality and patient safety. Accordingly, to ensure quality education and patient safety, the Ministry of Health mandated the anesthetist national licensing examination (NLE), which is relatively costly for low- and middle-income settings. However, empirical evidence is scarce to support or refute the appropriateness and usefulness of NLE-based pass-or-fail decisions. Therefore, this thesis investigates the broader impact of introducing the anesthetist NLE in Ethiopia, a low-income sub-Saharan African country. Using a pre- and post-evaluation design, chapter Two assesses changes in the quality of anesthetists' education due to integrated program interventions, including an NLE. Chapter Three closely explores the qualitative impact of implementing NLE on the quality of anesthetist education. Chapter Four delves further into the concerns and undesirable consequences of the NLE using a qualitative inquiry. Chapter Five quantifies student academic performance changes following the NLE by retrospectively gathering the academic records of anesthetists who graduated before and after NLE implementation. Chapter Six assesses the relationship between student academic performance and NLE scores and proposes academic performance thresholds that predict failing the NLE. Finally, Chapter Seven investigates the association between anesthetists' NLE scores and the quality of perioperative patient care they deliver. This thesis found that the anesthetist NLE has prompted anesthesia teaching institutions to improve their teaching-learning, assessment, and program quality improvement practices. Implementing NLE is also associated with a modest improvement in the academic performance of anesthetists. Gender disparities in academic performance disappeared following the NLE; even female students performed better in some measurements. The NLE score exhibits a linear relationship with most academic performance measures and an inverse association with the occurrence of critical incidents (including death), indicating that the exam is appropriate for deciding graduates’ readiness. Besides, based on pass/fail thresholds, the NLE could help training programs improve NLE pass rates. Meanwhile, some concerns and unintended consequences of the exam were identified, demanding more work to enhance the exam’s desirable impacts, fairness, and acceptability. On the other hand, the stagnant or declining academic performance among nurse entrants and recently opened university students warrant further investigation. Overall, the thesis findings will add to the existing literature and policymakers' understanding of task-sharing strategies and the implementation of an impactful NLE in Ethiopia and beyond. Regulatory authorities should enhance the impact of NLEs by verifying skill acquisition, enforcing consistent pass-or-fail decisions, and ensuring equitable access to exam-related information. Also, they should standardize education by accrediting training programs and mandating continuing professional development (CPD) to renew licenses. On the other hand, education sector stakeholders should focus on enhancing in-school student assessment systems, establishing targeted student support systems, and transforming training models to meet international standards. However, task-sharing and shifting strategies should not be considered quick fixes to specialist shortages; well-delineated roles and mandatory regulatory frameworks are critical. Future research agendas may include the impact of program accreditation and CPD.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".