Evaluation of a New Hierarchical Teaching Model for Pharmacy Students in Experiential Education
Notice bibliographique
Résumé
Pharmacy students undergoing experiential education have traditionally been mentored by pharmacist preceptors on a one-to-one basis. However, major curriculum changes are placing greater emphasis on experiential education. For example, the new PharmD curriculum within the University of Toronto’s Leslie Dan Faculty of Pharmacy requires Early Practice Experience (EPE) rotations of 160 h each after year 1 and year 2 (in place of the 12-h Early Hospital Experience [EHE] in year 2 of the previous program) and 36 weeks of experiential practice after year 3. Implementation of these changes has begun only within the past year (i.e., since mid-2012), and it will take some time to realize the full magnitude of the effect on staff workload at clinical sites; however, rotation requirements have already increased dramatically. In general, teaching sites strive to offer placements that are of high quality while accommodating the needs of pharmacist preceptors and clinical programs. This large increase in pharmacy student rotations has created a stress on the system and has forced sites to explore alternative methods of delivering experiential education. The hierarchical model of teaching, with senior students mentoring junior students, is well known in the medical field. However, little is known about how effective or feasible this model might be for pharmacy students. Lindblad and others described peer-assisted learning for final-year pharmacy students on a 9-week rotation in a general medicine/stroke care unit. In that model, the first group of 3 students started their rotation at week 1, with the second group of 3 students starting at week 5. This allowed for overlap and mentoring by the more experienced students during weeks 5 to 9. The second group then continued their rotation until week 13. However, such peer-assisted learning is based on more experienced students facilitating the learning of a group of peers who are all at the same point in their education. It may not be suitable for models in which students are at different points in their academic programs. Given the prospect of pharmacy students from different years completing rotations simultaneously, a hierarchical model may be one way to accommodate the increased demand for placements and to allow pharmacists to dedicate time to teaching in addition to their dispensing, clinical, and research duties. As described by Hall and others, pharmacy educators at Henry Ford Hospital in Detroit, Michigan, hypothesized that a hierarchical system similar to the medical model could be successful. Under this structure, pharmacy students at various levels (e.g., students, residents, fellows) would be under the supervision of an “attending” practitioner, who would be most responsible for the quality of patient care even as the students progressively assumed greater independence and more responsibilities. However, the changing availability of students from month to month was recognized as a potential barrier to full implementation of the system. Here, we report an evaluation of a hierarchical teaching model in which fourth-year pharmacy students and residents mentored junior pharmacy students under the supervision of a pharmacist, in terms of perceived impact on learning, overall experience, and pharmacists’ workload.
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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 | 0,019 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».