Innovative Mobile App (CPD By the Minute) for Continuing Professional Development in Medicine: Multimethods Study
Notice bibliographique
Résumé
BACKGROUND: Many national medical governing bodies encourage physicians to engage in continuing professional development (CPD) activities to cultivate their knowledge and skills to ensure their clinical practice reflects the current standards and evidence base. However, physicians often encounter various barriers that hinder their participation in CPD programs, such as time constraints, a lack of centralized coordination, and limited opportunities for self-assessment. The literature has highlighted the strength of using question-based learning interventions to augment physician learning and further enable change in practice. CPD By the Minute (CPD-Min) is a smartphone-enabled web-based app that was developed to address self-assessment gaps and barriers to engagement in CPD activities. OBJECTIVE: This study aimed to assess the app using four objectives: (1) engagement and use of the app throughout the study, (2) effectiveness of this tool as a CPD activity, (3) relevance of the disseminated information to physicians' practice, and (4) acceptability to physicians of this novel tool as an educational initiative. METHODS: The CPD-Min app disseminated 2 multiple-choice questions (1-min each) each week with feedback and references. Participants included licensed staff physicians, fellows, and residents across Canada. A concurrent multimethods study was conducted, consisting of preintervention and postintervention surveys, semistructured interviews, and app analytics. Guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, the qualitative data were analyzed deductively and inductively. RESULTS: Of the 105 Canadian anesthesiologists participating in the study, 89 (84.8%) were staff physicians, 12 (11.4%) were fellows, and 4 (3.8%) were residents. Participants completed 110 questions each over the course of 52 weeks, with an average completion rate of 75% (SD 33%). In total, 40.9% (43/105) of participants answered >90% of the questions, including 15.2% (16/105) who completed all questions. Moreover, 69% (52/75) of participants reported the app to be an effective and valuable resource for their practice and to enhance continuous learning. Most participants (63/75, 84%) who completed the postsurveys reported that they would likely continue using the app as a CPD tool. These findings were further supported by the interview data. Three key themes were identified: the practical design of the novel educational app facilitates its adoption by clinicians, the app was perceived as a useful knowledge tool for continuous learning, and the app's low-stakes testing environment cultivated independent learning attitudes. CONCLUSIONS: The findings suggest the potential of the app to improve longitudinal assessments that promote lifelong learning among clinicians. The positive feedback and increased acceptance of the app supports it as an innovative tool for knowledge retention and CPD. Future research efforts should prioritize evaluating the app's long-term sustainability and its impact on physicians' practice, as well as exploring alternative approaches (such as artificial intelligence-based tools) for generating questions.
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 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 | 0,007 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».