Using Group Concept Mapping to Explore Medical Education’s Blind Spots
Notice bibliographique
Résumé
PHENOMENON: All individuals and groups have blind spots that can lead to mistakes, perpetuate biases, and limit innovations. The goal of this study was to better understand how blind spots manifest in medical education by seeking them out in the U.S. APPROACH: We conducted group concept mapping (GCM), a research method that involves brainstorming ideas, sorting them according to conceptual similarity, generating a point map that represents consensus among sorters, and interpreting the cluster maps to arrive at a final concept map. Participants in this study were stakeholders from the U.S. medical education system (i.e., learners, educators, administrators, regulators, researchers, and commercial resource producers) and those from the broader U.S. health system (i.e., patients, nurses, public health professionals, and health system administrators). All participants brainstormed ideas to the focus prompt: "To educate physicians who can meet the health needs of patients in the U.S. health system, medical education should become less blind to (or pay more attention to) …" Responses to this prompt were reviewed and synthesized by our study team to prepare them for sorting, which was done by a subset of participants from the medical education system. GCM software combined sorting solutions using a multidimensional scaling analysis to produce a point map and performed cluster analyses to generate cluster solution options. Our study team reviewed and interpreted all cluster solutions from five to 25 clusters to decide upon the final concept map. FINDINGS: Twenty-seven stakeholders shared 298 blind spots during brainstorming. To decrease redundancy, we reduced these to 208 in preparation for sorting. Ten stakeholders independently sorted the blind spots, and the final concept map included 9 domains and 72 subdomains of blind spots that related to (1) admissions processes; (2) teaching practices; (3) assessment and curricular designs; (4) inequities in education and health; (5) professional growth and identity formation; (6) patient perspectives; (7) teamwork and leadership; (8) health systems care models and financial practices; and (9) government and business policies. INSIGHTS: Soliciting perspectives from diverse stakeholders to identify blind spots in medical education uncovered a wide array of issues that deserve more attention. The concept map may also be used to help prioritize resources and direct interventions that can stimulate change and bring medical education into better alignment with the health needs of patients and communities.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».