A response to “Competency frameworks: universal or local” by Mortaz Hejri and Jalili (2012)
Bibliographic record
Abstract
First, we would like to thank Hejri and Jalili (Hejri and Jalili 2013) for endorsing the main point in our critical review of outcome (competency) based education (Morcke et al. 2012). We agree with the two points they make: that global prescriptions of curriculum content can be insensitive to cultural differences; and that effective curriculum development calls for local engagement. For the record, the phrase they criticise is not one we wrote, but the conclusion of other authors whose work we reviewed. Their point, nevertheless, is important enough to deserve a reaction. It is one thing to say the CanMEDS competency framework has been extensively adopted around the world. It is another thing to say CanMEDS is equally suited to all cultures. What can be said, however, is that it travels well! A framework, which was developed for use in Canada, has been adopted in Denmark as a framework across preand postgraduate training and across all four medical schools, which have different curricula and different approaches to learning. In the Netherlands, likewise, CanMEDS has provided useful as a national organizing framework for competency based postgraduate education. The secret of its success may be that it leaves the scope for local interpretation, which Hejri and Jalili (Hejri and Jalili 2013) ask for. But therein lies a big problem of competency-based education. What is gained in generalizability is lost in specificity. Our review did not conclude that all medical schools around the world should adopt the same framework. To the contrary, we agree with Hejri and Jalili that medical schools ‘‘should evaluate the risks and benefits of developing a new set of outcomes before adopting an existing framework.’’ What we did conclude was that outcome-based
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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 | 0.072 | 0.216 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.042 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.080 | 0.148 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".