Otolaryngology–head and neck surgery in undergraduate medical education: Advances and innovations
Notice bibliographique
Résumé
OBJECTIVES/HYPOTHESIS: Medical students graduate with the knowledge and skills to be undifferentiated general physicians. Otolaryngology-head and neck surgery (OtoHNS) is an essential component of primary healthcare, but is disproportionately under-represented in undergraduate medical education (UME). Advances and innovations in educational technology may represent an exciting and creative solution to this important problem. Failure to meet this educational need will result in substantial downstream effects in primary healthcare delivery. The objectives of this study were to 1) demonstrate current deficits in OtoHNS teaching at the UME level; 2) develop, validate, and critically appraise educational innovations that may enrich OtoHNS teaching in medical school curricula; and 3) propose a process for standardization of learning objectives for OtoHNS in UME as it relates to development and deployment of such educational tools. STUDY DESIGN: A white paper, prepared as a Triological Society thesis, which consolidates a prospective 10-year investigation of the problem of and potential solutions for under-representation of OtoHNS in UME. Cited datasets include multicenter surveys, cohort studies, and prospective, randomized controlled trials. METHODS: A series of published and unpublished data were synthesized that addresses the following: 1) the current state of OtoHNS teaching at the UME level with respect to content, volume, structure, and methods; and 2) educational innovations including e-learning and simulation with emphasis on validity and learning effectiveness. Educational innovations specific to postgraduate (residency) training were excluded. RESULTS: Data support the observation that there is uniformly disproportionate under-representation of OtoHNS within UME curricula. Medical school graduates, especially those pursuing primary care specialties, report poor overall comfort levels in managing OtoHNS problems. A series of novel teaching methods were developed and validated using e-learning and simulation. Selected technologies may have a role in medical student teaching. It has been shown that e-learning has limited value in teaching complex spatial anatomy to novice learners, but good value in teaching basic clinical knowledge and selected technical skills. The role of simulation as it pertains to the novice learner is evolving. Important factors to consider during development of these tools include: 1) knowledge base and learning style of the learner, 2) complexity and nature of the learning objectives, 3) understanding the features and limitations of different technological genres, and 4) a team approach to module development. There remains a role for traditional teaching paradigms such as lectures, labs, and standardized patients; however, the choice of instructional genre should be fundamentally tailored to the nature of the learning outcomes. CONCLUSIONS: Enriching OtoHNS teaching in medical school is essential optimize primary care delivered to patients. Although e-learning and simulation are broadly accepted and desirable by today's medical students, these technologies should be woven into the fabric of UME pedagogical principles judiciously, and only after empiric assessment. Foundational to the development and implementation of these technologies is the framework of standardized competency-based learning objectives, common to all graduating medical students. LEVEL OF EVIDENCE: NA
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 | 0,027 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».