CME programme leads to considerations in design concepts
Bibliographic record
Abstract
Context and setting Ongoing professional learning is essential for all health professionals, although there are limitations to the current educational approaches. Continuing medical education (CME) providers must learn to challenge entrenched strategies and meld the successful parts of traditional interventions with strategies that ‘push the envelope’ and force participants into a position of accepting that maybe not knowing is acceptable and ongoing learning is what counts. Creating this environment is difficult with doctors because traditionally they have held positions of authority in their communities and because they make important decisions. Continuing medical education programmes that present the doctor with disorienting dilemmas can create emotional responses that help doctors to realise their unperceived needs. Team approaches to learning can represent ways of assimilating and translating information into clinical practice. In order to do so, doctors need to feel comfortable relying on others and with the idea of being a student. Why the idea was necessary The ‘Weakest Link’, the first programme of its kind in Canada, was initially developed by Merck Frosst Canada as an educational tool for general practitioners to spice up their CME programmes. It was so well received that the University of Alberta and Merck Frosst decided to measure its impact on the specialist audience at a national specialty society conference. We felt this intervention was necessary to breathe new life into the traditional CME format and to observe whether a competitive, evidence-based, game-style CME tool would help doctors acknowledge their hitherto unperceived needs and perhaps alter their self-perception of always ‘needing to be right’. What was done A time-limited, competitive intervention was created using evidence-based true or false questions that were constructed by a scientific committee in response to a needs assessment conducted with 100 Canadian rheumatologists. In fact, the questions were intentionally constructed to facilitate controversy and provoke reflection. A total of 70 participants sat in teams and team consensus was necessary to cast the answer. The teams were scored and the team with the most correct answers was announced as the strongest link, while the team with the least correct answers was decreed the weakest link. The programme was evaluated and the major themes were extracted. Evaluation of results and impact The programme design elements embodied controversy and created healthy competition. We hypothesise that the controversial nature of the questions that resulted in disagreement acted as a trigger as doctors unexpectedly realised that they did not know all the correct answers. This led to debate within their teams and with the content experts. Some doctors expressed disagreement with the answers, even when the latter were supported by the evidence. Ultimately, their unperceived learning needs were revealed through exposure to a stimulating learning environment. This is a strong feature of this programme's success. The programme evoked enthusiasm among participants, transforming them into learners, although for some this was uncomfortable. Furthermore, we hypothesise that the disagreement and controversy ultimately led to improved learning and retention. It may be that the combination of wanting to be right and needing to be right is what ultimately turns all of us into learners.
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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 | 0.059 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".