The anatomy of healthcare student perceptions
Notice bibliographique
Résumé
Feedback is a vital part of any healthcare career. Recognizing the importance of feedback, many healthcare professional schools are implementing reflection exercises into the curriculum. The gross anatomy lab, with its team‐oriented nature, fosters an environment conducive to developing the skills of giving and receiving feedback. The purpose of our project was to evaluate differences between how medical and allied health students perceive themselves and their peers. We hypothesized that medical and allied health students would rate themselves lower than their peers and that medical and allied health students would emphasize different themes in their narrative comments. As a part of the anatomy curriculum, students are required to complete self‐reflection and peer feedback exercises consisting of 5 rating rubric statements and sections for narrative comments on strengths and areas for improvement at the end of the course or semester. These exercises address medical knowledge, professionalism, communication, and practice‐based learning domains. We evaluated the responses of medical (n=192) and allied health (n=123) students by averaging the rating rubric responses and sorting the narrative comments into three main themes (professionalism and leadership skills, knowledge, and personal behaviors). When analyzing the rating rubric statements, there was little difference in how medical and allied health students rated their peers or themselves between the two programs. However, both medical and allied health students consistently rated themselves significantly lower than their peers. When evaluating narrative comments on strengths, students commented predominantly on professionalism and leadership traits with little difference between medical and allied health students. However, medical students emphasized knowledge more than allied health students (Peer: 16.7% vs 10.9%; Self: 10.4% vs 6.8%; p<0.01). Conversely, medical students emphasized personal behaviors less than allied health students (Peer: 22.2% vs 27.6%; Self: 19.8% vs 27.5%; p<0.01). When evaluating narrative comments on areas for improvement, medical students emphasized professionalism and leadership skills more than allied health students (Peer: 51.1% vs 42.7%; Self: 38.3% vs 27.3%; p<0.01). Again, medical students emphasized personal behaviors less than allied health students (Peer: 28.9% vs 40.0%; Self: 22.8% vs 36.4%; p<0.01). When evaluating areas of improvement, medical and allied health students emphasized knowledge and preparation similarly. Overall, there are significant differences in the emphasis placed on personal behaviors between medical and allied health students. When commenting on personal behaviors as strengths, both groups emphasized extrinsic behaviors (respect, patience, enthusiasm, and engagement). Whereas, when students commented on personal behaviors that needed improvement, both groups emphasized intrinsic behaviors (frustration, negativity, and lack of confidence). The differences found in emphasis between programs may be due to variances between department and program objectives, the timing and intensity of the anatomy courses, the degree level of the programs, and/or the varying proportions of males and females within the programs. Understanding how medical and allied health students perceive themselves and others may give us insight into methods to improve multidisciplinary communication within the healthcare setting.
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 | 0,006 | 0,032 |
| 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 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».