Assessment approaches in undergraduate health professions education: towards the development of feasible assessment approaches for low-resource settings
Bibliographic record
Abstract
BACKGROUND: Feasible and effective assessment approaches to measuring competency in health sciences are vital in competency-based education. Educational programmes for health professions in low- and middle-income countries are increasingly adopting competency-based education as a strategy for training health professionals. Importantly, the organisation of assessments and assessment approaches must align with the available resources and still result in the fidelity of implementation. A review of existing assessment approaches, frameworks, models, and methods is essential for the development of feasible and effective assessment approaches in low-resource settings. METHODS: Published literature was sourced from 13 electronic databases. The inclusion criteria were literature published in English between 2000 and 2022 about assessment approaches to measuring competency in health science professions. Specific data relating to the aims of each study, its location, population, research design, assessment approaches (including the outcome of implementing such approaches), frameworks, models, and methods were extracted from the included literature. The data were analysed through a multi-step process that integrated quantitative and qualitative approaches. RESULTS: Many articles were from the United States and Australia and reported on the development of assessment models. Most of the articles included undergraduate medical or nursing students. A variety of models, theories, and frameworks were reported and included the Ideal model, Predictive Learning Assessment model, Amalgamated Student Assessment in Practice (ASAP) model, Leadership Outcome Assessment (LOA) model, Reporter-Interpreter-Manager-Educator (RIME) framework, the Quarter model, and the model which incorporates four assessment methods which are Triple Jump Test, Essay incorporating critical thinking questions, Multistation Integrated Practical Examination, and Multiple Choice Questions (TEMM) model. Additional models and frameworks that were used include the Entrustable Professional Activities framework, the System of Assessment framework, the Reporter-Interpreter-Manager-Educator (RIME) framework, the Clinical Reasoning framework (which is embedded in the Amalgamated Student Assessment in Practice (ASAP) model), Earl's Model of Learning, an assessment framework based on the Bayer-Fetzer Kalamazoo Consensus Statement, Bloom's taxonomy, the Canadian Medical Education Directions for Specialists (CanMEDS) Framework, the Accreditation Council for Graduate Medical Education (ACGME) framework, the Dreyfus Developmental Framework, and Miller's Pyramid. CONCLUSION: An analysis of the assessment approaches, frameworks, models, and methods applied in health professions education lays the foundation for the development of feasible and effective assessment approaches in low-resource settings that integrate competency-based education. TRIAL REGISTRATION: This study did not involve any clinical intervention. Therefore, trial registration was not required.
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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 | 0.320 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".