Assessment approaches in undergraduate health professions education: towards the development of feasible assessment approaches for low-resource settings
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».