Just in Time Teaching (JiTT) Infographics App for Teacher Development
Notice bibliographique
Résumé
During clinical clerkships, medical students credit residents for enhancing their clinical knowledge. Residents can spend at least 25% of their time teaching medical students, and many training programs consider resident-as-teacher skills a core competency. Despite this key role, many residents lack adequate instructions and training in teaching and mentoring skills and methodologies.Given the growing demand to enhance the existing curriculum among our residents and faculty (concomitant with requirements set forth by accreditation bodies), faculty development is moving to Just in Time Teaching models for content delivery. The ubiquitous use and availability of smartphones and connectivity through applications (apps) has tremendous potential to enhance the ability of trainees and faculty to supervise learners on their clinical teams with evidence-based knowledge and skills across the continuum of medical education. Geographically dispersed academic health systems, which continue to grow, require access to educational resources to guide diverse teaching needs of clinicians.The Just in Time Teaching (JiTT) Infographics app was created by combining a pedagogical approach derived from the SAMR (substitution, augmentation, modification, redefinition) technological conceptual framework. The JiTT Infographics app is a novel teaching tips approach that delivers evidence-based clinically relevant teaching tips to trainees and clinical faculty in their environment and can be used alone or adapted for resident-as-teacher programs.Each JiTT infographic tool supports clinical teachers' access to faculty development with an asynchronous digital experience strategy to engage busy teachers in a geographically distributed medical education network.1 Educational content was developed by medical educators, clinician educators, and residents. Each JiTT tool is also downloadable as a PDF to explain foundational and specialty-specific clinical teaching tips during didactic sessions. JiTTs can be saved in a “Favorites” category to ease finding a JiTT in the moment of need.Foundational JiTT tools include brief podcasts to support learners who prefer listening to content. Foundational teaching principles include setting expectations, questioning techniques, feedback and coaching, and bedside teaching. Specific clinical teaching techniques include content pertaining to internal medicine, family medicine, pediatrics, obstetrics and gynecology, surgery, psychiatry, and neurology, as well as sub-specialties. In addition, categories focused on teaching wellness, quality, social justice, and research principles are included. Optional review questions are provided in each category for users to self-assess their acquired knowledge.The JiTT Infographics app is available in Google Play and the Apple Store. As of April 2022, the app has been downloaded by 3406 unique users across 90 countries, with the largest numbers of downloads from the United States (N=2337), Phillippines (N=179), Mexico (N=169), Canada (N=98), and Saudia Arabia (N=68). Preliminary analysis of app activity highlights the foundational teaching category, which includes: Domains of Social Determinants of Health; 5 Micro Skills: Precept with Limited Time; and Questioning as an Effective Teaching Skill. The results to date support a global interest in utilizing technology to increase accessibility to open access resources. As of April 2022, a brief video on YouTube (July 2021) that guides users on how to apply the JiTT in real-time teaching settings has been viewed 462 times. In March 2022, the app was accredited as an “enduring product,” enabling interprofessional users to obtain selected continuing education credits at no cost. The app as an evidence-based JiTT teaching tool is an outcome in itself, as it redfines technology for faculty development.Future efforts will focus on app evaluation by capturing end-user feedback via brief structured feedback surveys and more in-depth analyses of learner engagement metrics to determine the outcomes and effectiveness of the app. Requests for translation to other languages are in the pipeline, as are additional external collaborators.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,081 | 0,026 |
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 ».