A Case Study Exploring How Professional Education Programs at a Mid-sized Canadian University are Conceptualizing and Operationalizing Entry-to-practice Competence Frameworks
Bibliographic record
Abstract
Entry-to-practice competence frameworks and competency-based approaches to professional education are becoming increasingly popular in Canada and on a global scale. Although competency-based medical education has the potential to inform approaches to the development and assessment of competence across professional disciplines, there are contextual factors which make medical education unique. To date, few studies have compared how university-based professional education programs are using competence frameworks to guide teaching/learning and assessment in their own professional contexts. Consequently, the purpose of this qualitative case study was to explore how professional education programs at a mid-sized Canadian university are conceptualizing and operationalizing entry-to-practice competence frameworks. In Study 1, theoretical tensions between behavioural and integrated conceptions of competence were explored by comparing similarities/differences across ten professions’ entry-to-practice competence frameworks. In Study 2, an in-depth interpretive case study approach was used to explore how the assessment of competence is being operationalized in a highly resourced and work-integrated professional education program. Finally, in Study 3, an embedded case study was used to explore how nine different professional programs, with potentially fewer-resources and work-integrated learning opportunities, are approaching and perhaps problematizing the development and assessment of competence. Taken altogether, the findings of Studies 1, 2, and 3 suggest that how competence is conceptualized and represented matters and has the potential to shape how competence is developed and assessed at the program level. While limited in scope given the use of a single university, the findings highlight: (1) diversity in the approaches to operationalization being used across programs; (2) common attributes which can be used to classify the manner in which these programs operationalize the development and assessment competence; and (3) challenges with supporting academic faculty, who have academic freedom, to buy in to competence as a construct informing pedagogy and assessment. These findings can be used to inform policy and practice decisions about: (1) the role professional programs play in determining competence for entry-to-practice along professional pathways to licensure, and (2) programs’ intents for and approaches to operationalizing entry-to-practice competence frameworks in practice.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".