Dichotomy and dialogue in conceptualizations of competency in health professionals’ education
Bibliographic record
Abstract
Most research on the definition of competency and its application in health professionals’ education programs is focused on semantic matters, while contextual influences are rarely discussed or solely presented as background context. The aim of this article is to gain a comprehensive understanding of the issues raised by definitions of competency and to describe the contextual factors that have given rise to those definitions. This is achieved by presenting the results of a literature review that synthesized different conceptualizations of competency. We analyzed relevant literature listed in the CINAHL, Embase, MEDLINE, and PsycINFO databases as of 2015. The results show that authors define competency based on two diverging driving forces: one aimed at professional regulation and the other at professional emancipation. The analysis revealed common grounds between these perspectives. From these common grounds we discuss the possibility of conceptualizing competency on a continuum instead of perpetuating the dichotomized discourses presented in the current literature. The integration of both perspectives gives the opportunity to rethink policies, structures and strategies of professional education toward an integrated perspective of professional development situated in a lifelong learning enterprise, achieving both minimal professional standards and excellence in healthcare practices from initial education throughout a career.
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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 | 0.048 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".