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Enregistrement W2111759442 · doi:10.1111/j.1365-2929.2006.02465.x

CME programme leads to considerations in design concepts

2006· article· en· W2111759442 sur OpenAlexaboutno aff
Paul Davis, Sherry Robertson, Angela Juby

Notice bibliographique

RevueMedical Education · 2006
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Psychological interventionPublic relationsIntervention (counseling)SpecialtyMedical educationPsychologyContinuing medical educationOrder (exchange)MedicineNursingContinuing educationPolitical scienceBusinessFamily medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,059
score de la tête « metaresearch » (Gemma)0,088
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,059
Score d'incertitude au seuil0,313

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0590,088
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,011
Communication savante0,0100,010
Science ouverte0,0040,004
Intégrité de la recherche0,0050,006
Charge utile insuffisante (le modèle a refusé de juger)0,0200,004

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,388
Écart entre enseignants0,359 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2006
Routes d'admission1
Résumé présentoui

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