Evaluating Change in Student Pharmacists’ Familiarity, Attitudes, Comfort, and Knowledge as a Result of Integrating Digital Health Topics Into a Case Conference Series: Cohort Study
Notice bibliographique
Résumé
BACKGROUND: The use of technology in health care, often referred to as digital health, has expanded rapidly because of the need to provide remote care during the COVID-19 pandemic. In light of this rapid boom, it is clear that health care professionals need to be trained in these technologies in order to provide high-level care. Despite the growing number of technologies used across health care, digital health is not a commonly taught topic in health care curricula. Several pharmacy organizations have called attention to the need to teach digital health to student pharmacists; however, there is currently no consensus on best methods to do so. OBJECTIVE: The objective of this study was to determine if there was a significant change in student pharmacist scores on the Digital Health Familiarity, Attitudes, Comfort, and Knowledge Scale (DH-FACKS) after exposure to digital health topics in a yearlong discussion-based case conference series. METHODS: Student pharmacists' initial comfort, attitudes, and knowledge were gathered by a baseline DH-FACKS score at the beginning of the fall semester. Digital health concepts were integrated into a number of cases in the case conference course series throughout the academic year. The DH-FACKS was administered again to students after completion of the spring semester. Results were matched, scored, and analyzed to assess any difference in DH-FACKS scores. RESULTS: A total of 91 of 373 students completed both the pre- and postsurvey (response rate of 24%). Using a scale from 1 to 10, the mean student-reported knowledge of digital health increased from 4.5 (SD 2.5) before intervention to 6.6 (SD 1.6) after intervention (P<.001) and the mean self-reported comfort increased from 4.7 (SD 2.5) before intervention to 6.7 (SD 1.8) after intervention (P<.001). There was a significant increase in scores for all 4 elements of the DH-FACKS. The mean familiarity scores increased from 11.6 (SD 3.7) to 15.8 (SD 2.2), out of a maximum of 20 (P<.001). The mean attitudes scores increased from 15.6 (SD 2.1) to 16.5 (SD 1.9), out of a maximum of 20 (P=.001). The mean comfort scores increased from 10.1 (SD 3.9) to 14.8 (SD 3.1), out of a maximum of 20 (P<.001). The mean knowledge scores increased from 9.9 (SD 3.4) to 12.8 (SD 3.9), out of a maximum of 20 (P<.001). CONCLUSIONS: Including digital health topics in a case conference series is an effective and approachable way of providing education on important digital health concepts to students. Students experienced an increase in familiarity, attitudes, comfort, and knowledge after the yearlong intervention. As case-based discussions are an important component of most pharmacy and other medical curricula, this method can be easily applied by other programs that wish to give their students practice applying their knowledge of digital health to complex case-based scenarios.
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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 | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».