Explicit Connections Between Anatomy and Clinical Science are Key to Successful Cognitive Integration
Notice bibliographique
Résumé
Introduction The importance of integrated basic and clinical science knowledge is well recognized across the health professions; however, supporting the development of learner’s integrated knowledge in day‐to‐day teaching continues to be an educational challenge. A number of experimental studies show that learners can benefit when instruction is designed to support to support cognitive integration. This type of integration occurs at the individual session‐level and can be contrasted with integration efforts that occur within the curriculum. To date, there remains limited research on specific strategies that educators can use to support learners in achieving cognitive integration during individual teaching sessions. In a series of studies, we examine the relative impact of integrating anatomy and clinical science instruction with different learning strategies on novices learning diagnosis of musculoskeletal (MSK) pathologies. Methods In each study, novice learners were taught four MSK upper limb pathologies using different instructional approaches (integrated anatomy and clinical science, segregated anatomy and clinical science, or clinical science only) combined with additional learning strategies (focused self‐explanation, feature counting, holistic self‐explanation, or worked‐examples). Integrated instruction involved explicitly teaching the underlying causal mechanisms for the signs and symptoms associated with each MSK pathology, whereas no explicit linkages were provided in the segregated or clinical science only learning conditions. Immediately after learning and one‐week later, learners completed a diagnostic accuracy test. Results The findings of these studies showed that novices who learned the MSK pathologies using integrated instructional materials developed superior diagnostic abilities compared to those in the segregated or clinical science only learning groups (p < 0.05). Further, learners who also engaged in holistic self‐explanation while learning with integrated instructional materials scored higher on the diagnostic accuracy test compared those who simply read through worked‐examples that highlighted the underlying anatomical pathology associated with each MSK condition (p < 0.05). Accuracy on the holistic self‐explanation task was also positively correlated with learners diagnostic scores one‐week after initial learning (r = 0.457). Conclusions Our findings demonstrate the value of providing explicit connections between anatomy and clinical science in supporting deep learning in novices. This research also highlights the value of designing instruction that supports cognitive integration and demonstrates that one to maximize learning of anatomy is to use it as a tool to help learners more effectively understand and organize clinical concepts. Further, our findings suggest that learning strategies that emphasize the explicit connections between anatomy and clinical science in a holistic way, hold the potential to support the development of learners’ conceptual knowledge.
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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 | 0,002 | 0,016 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».