CME programme leads to considerations in design concepts
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,059 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,011 |
| Communication savante | 0,010 | 0,010 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».