Varying conceptions of competence: an analysis of how health sciences educators define competence
Bibliographic record
Abstract
CONTEXT: Current debate in medical education focuses on the nature of 'competency-based medical education' (CBME) and whether or not it should be adopted. Many medical schools claim to run 'competency-based' curricula, but the structure of their programmes can differ radically. A review of the existing CBME literature reveals that little attention has been paid to defining the concept of competence. A straightforward examination of what is meant by the term 'competence' is noticeably missing from the literature, despite its impact on medical training. OBJECTIVES: This paper aims to illustrate the varying conceptions of 'competence' by comparing and contrasting definitions provided in the health sciences education literature and discussing their respective impacts on medical education. METHODS: A systematic review of recent publications in medical education journals published in English and French was conducted to extract definitions of competence or, if definitions were not explicitly stated, to derive the authors' implicit conception of competence. A sample of 14 definitions from articles in the health sciences education field was studied using thematic analysis. RESULTS: There is agreement that competence is composed of knowledge, skills and other components. Although agreement about the nature of these other components is lacking, attitudes and values are suggested to be essential ingredients of competence. Furthermore, a clear divergence in conceptions of how a competent person utilises these components is apparent. One view specifies that competence involves selecting components according to specific situations, as required. A second view places greater emphasis on the synergy that results from the use of a combination of components in a given situation. CONCLUSIONS: These conceptual distinctions have many implications for the way CBME is implemented. A conception of competence as the selection of components may lead to a greater emphasis, in a training setting, on the mastery of each component separately. A conception of competence as the use of a combination of components leads to greater emphasis on the synergy that results as they are deployed in clinical situations.
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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 | 0.091 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".