Bibliographic record
Abstract
Stepping off the rapidly moving plat-form that has been the job of the associate dean of continuing medical education (CME) at the University of Toronto since the mid 1990s gives me an opportunity for “reflection-on-action.” This is a neat concept, articulated best by Schon, more often applied to clinical situations where we learn by thinking about a past encounter (e.g., in the emergency room)—what went right, what went wrong and how we might improve the outcome. This is an internal quality improvement. This point in time also provides me with the great luxury of being able to write to my family practice colleagues across the country as an open letter to the primary care CME community. After messing with it over the last decade at the University of Toronto, here’s my take on the CME scene in Canada. There is plenty to comment on that is good. I’ll list some of the accom-plishments which have made CME bet-ter, especially those that I’ve seen at the University, including: • more workshops and small group learning activities; • better needs assessments; • standard additions of useful handout materials; • more alternatives to the standard course, such as video-conferencing or Webcasting; • more interprofessional education; • more in-depth workshops, often in topics frequently ignored by industry (i.e., communication skills training, palliative care and psychosocial issues); • better interactive lecturing and • some strides in self-directed learning. Some of us have even begun to tack-le public education. Our colleagues in the country’s other medical schools and the two professional colleges have also done a heck of a job—I note the Maintenance of Certification Program of the Royal College, in particular.1 But there’s some not-so-good out there, too. In many ways, despite all its success, I would say that organized CME has failed miserably to help fami-ly physicians (and many others, includ-ing patients) in the way they need it. Here are some cases in point:
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.589 | 0.438 |
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".