Occupational Therapy Students’ Evidence-Based Practice Skills as Reported in a Mobile App: Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Evidence-based practice (EBP) is an important aspect of the health care education curriculum. EBP involves following the 5 EBP steps: ask, assess, appraise, apply, and audit. These 5 steps reflect the suggested core competencies covered in teaching and learning programs to support future health care professionals applying EBP. When implementing EBP teaching, assessing outcomes by documenting the student's performance and skills is relevant. This can be done using mobile devices. OBJECTIVE: The aim of this study was to assess occupational therapy students' EBP skills as reported in a mobile app. METHODS: We applied a cross-sectional design. Descriptive statistics were used to present frequencies, percentages, means, and ranges of data regarding EBP skills found in the EBPsteps app. Associations between students' ability to formulate the Population, Intervention, Comparison, and Outcome/Population, Interest, and Context (PICO/PICo) elements and identifying relevant research evidence were analyzed with the chi-square test. RESULTS: Of 4 cohorts with 150 students, 119 (79.3%) students used the app and produced 240 critically appraised topics (CATs) in the app. The EBP steps "ask," "assess," and "appraise" were often correctly performed. The clinical question was formulated correctly in 53.3% (128/240) of the CATs, and students identified research evidence in 81.2% (195/240) of the CATs. Critical appraisal checklists were used in 81.2% (195/240) of the CATs, and most of these checklists were assessed as relevant for the type of research evidence identified (165/195, 84.6%). The least frequently correctly reported steps were "apply" and "audit." In 39.6% (95/240) of the CATs, it was reported that research evidence was applied. Only 61% (58/95) of these CATs described how the research was applied to clinical practice. Evaluation of practice changes was reported in 38.8% (93/240) of the CATs. However, details about practice changes were lacking in all these CATs. A positive association was found between correctly reporting the "population" and "interventions/interest" elements of the PICO/PICo and identifying research evidence (P<.001). CONCLUSIONS: We assessed the students' EBP skills based on how they documented following the EBP steps in the EBPsteps app, and our results showed variations in how well the students mastered the steps. "Apply" and "audit" were the most difficult EBP steps for the students to perform, and this finding has implications and gives directions for further development of the app and educational instruction in EBP. The EBPsteps app is a new and relevant app for students to learn and practice EBP, and it can be used to assess students' EBP skills objectively.
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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 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».