How are Professional Programs from Diverse Disciplines Approaching the Development and Assessment of Competence at a Mid-Sized Canadian University?
Bibliographic record
Abstract
Time-honoured university policies, such as the credit-hour and academic freedom, present challenges for professional education programs tasked with operationalizing entry-to-practice competence frameworks for professional accreditation. A single embedded case study was used to explore how professional programs from one mid-sized Canadian university are approaching and perhaps problematizing the development and assessment of competence. Semi-structured interviews were conducted with educational leaders (faculty and staff, n=21) from a sample of nine programs. Following a grounded theory approach to qualitative analysis, the constant comparative method was used to inductively discern similarities and differences across programs, and to begin building theory about approaches to operationalization. While limited in scope given the use of a single university, our findings highlight: (a) diversity in approaches to operationalization across programs, (b) common attributes which can be used to classify the manner in which these programs operationalize competence, and (c) challenges with supporting faculty to buy in to competency-informed pedagogy and assessment. Given these findings, it is recommended that professional accrediting bodies and education programs spend time to consider the role university-based programs play in determining competence for entry-to-practice, as well their intents for implementing a competence framework, to ensure sufficiency in the approaches being used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 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".