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Enregistrement W2946600278 · doi:10.2196/12791

Why Medical Students Choose to Use or Not to Use a Web-Based Electrocardiogram Learning Resource: Mixed Methods Study

2019· article· en· W2946600278 sur OpenAlexvenueno aff
Mikael Nilsson, Uno Fors, Jan Östergren, Gunilla Bolinder, Samuel Edelbring

Notice bibliographique

RevueJMIR Medical Education · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueECG Monitoring and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesStockholms Läns Landsting
Mots-clésCompetence (human resources)The InternetMedical educationResource (disambiguation)PsychologyComputer scienceMedicineKnowledge managementWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Electrocardiogram (ECG) interpretation is a core competence and can make a significant difference to patient outcomes. However, ECG interpretation is a complex skill to learn, and research has showed that students often lack enough competence. Web-based learning has been shown to be effective. However, little is known regarding why and how students use Web-based learning when offered in a blended learning situation. OBJECTIVE: The aim of this paper was to study students' use of Web-based ECG learning resources which has not previously been studied in relation to study strategies. METHODS: A qualitative explanatory design using mixed methods was adopted to explore how medical students reason around their choice to use or not to use a Web-based ECG learning resource. Overall, 15 of 33 undergraduate medical students attending a course in clinical medicine were interviewed. Data on usage of the resource were obtained via the learning management system for all students. At the final examination, all the students answered a questionnaire on study strategies and questions about internet access and estimated their own skills in ECG interpretation. Furthermore, study strategies and use patterns were correlated with results from an ECG Objective Structured Clinical Examination (OSCE) and a written course examination. RESULTS: In total, 2 themes were central in the students' reasoning about usage of Web-based ECG: assessment of learning needs and planning according to learning goals. Reasons for using the Web resource were to train in skills, regarding it as a valuable complement to books and lectures. The main reasons for not using the resource were believing they already had good enough skills and a lack of awareness of its availability. Usage data showed that 21 students (63%) used the Web resource. Of these, 11 were minimal users and 10 were major users based on usage activity. Large variations were found in the time spent in different functional parts of the resource. No differences were found between users and nonusers regarding the OSCE score, final examination score, self-estimate of knowledge, or favoring self-regulated learning. CONCLUSIONS: To use or not to use a Web-based ECG learning resource is largely based on self-regulated learning aspects. Decisions to use such a resource are based on multifactorial aspects such as experiences during clinical rotations, former study experiences, and perceived learning needs. The students' own judgment of whether there was a need for a Web-based resource to achieve the learning goals and to pass the examination was crucial for their decisions to use it or not. An increased understanding of students' regulation of learning and awareness of variations in their ECG learning needs can contribute to the improvement of course design for blended learning of ECG contexts for medical students.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,017
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,290
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,017
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,024
Tête enseignante GPT0,446
Écart entre enseignants0,422 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2019
Routes d'admission1
Résumé présentoui

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