Measuring the Quality of Clinical Skills Mobile Apps for Student Learning: Systematic Search, Analysis, and Comparison of Two Measurement Scales
Notice bibliographique
Résumé
BACKGROUND: Mobile apps are widely used in health professions, which increases the need for simple methods to determine the quality of apps. In particular, teachers need the ability to curate high-quality mobile apps for student learning. OBJECTIVE: This study aims to systematically search for and evaluate the quality of clinical skills mobile apps as learning tools. The quality of apps meeting the specified criteria was evaluated using two measures-the widely used Mobile App Rating Scale (MARS), which measures general app quality, and the Mobile App Rubric for Learning (MARuL), a recently developed instrument that measures the value of apps for student learning-to assess whether MARuL is more effective than MARS in identifying high-quality apps for learning. METHODS: Two mobile app stores were systematically searched using clinical skills terms commonly found in medical education and apps meeting the criteria identified using an approach based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 9 apps were identified during the screening process. The apps were rated independently by 2 reviewers using MARS and MARuL. RESULTS: The intraclass correlation coefficients (ICCs) for the 2 raters using MARS and MARuL were the same (MARS ICC [two-way]=0.68; P<.001 and MARuL ICC [two-way]=0.68; P<.001). Of the 9 apps, Geeky Medics-OSCE revision (MARS Android=3.74; MARS iOS=3.68; MARuL Android=75; and MARuL iOS=73) and OSCE PASS: Medical Revision (MARS Android=3.79; MARS iOS=3.71; MARuL Android=69; and MARuL iOS=73) scored highly on both measures of app quality and for both Android and iOS. Both measures also showed agreement for the lowest rated app, Patient Education Institute (MARS Android=2.21; MARS iOS=2.11; MARuL Android=18; and MARuL iOS=21.5), which had the lowest scores in all categories except information (MARS) and professional (MARuL) in both operating systems. MARS and MARuL were both able to differentiate between the highest and lowest quality apps; however, MARuL was better able to differentiate apps based on teaching and learning quality. CONCLUSIONS: This systematic search and rating of clinical skills apps for learning found that the quality of apps was highly variable. However, 2 apps-Geeky Medics-OSCE revision and OSCE PASS: Medical Revision-rated highly for both versions and with both quality measures. MARS and MARuL showed similar abilities to differentiate the quality of the 9 apps. However, MARuL's incorporation of teaching and learning elements as part of a multidimensional measure of quality may make it more appropriate for use with apps focused on teaching and learning, whereas MARS's more general rating of quality may be more appropriate for health apps targeting a general health audience. Ratings of the 9 apps by both measures also highlighted the variable quality of clinical skills mobile apps for learning.
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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 | 0,106 | 0,314 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,015 |
| Bibliométrie | 0,027 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».