Effectiveness of an Interactive Web-Based Clinical Practice Monitoring System on Enhancing Motivation in Clinical Learning Among Undergraduate Nursing Students: Longitudinal Quasi-Experimental Study in Tanzania
Notice bibliographique
Résumé
BACKGROUND: Nursing students' motivation in clinical learning is very important not only for their academic and professional achievement but also for making timely, informed, and appropriate decisions in providing quality and cost-effective care to people. However, the increased number of students and the scarcity of medical supplies, equipment, and patients, just to mention a few, have posed a challenge to educators in identifying and navigating the best approaches to motivate nursing students to learn during their clinical placements. OBJECTIVE: This study primarily used descriptive and analytical methods to examine undergraduate nursing students' desire for clinical learning both before and after participating in the program. METHODS: An uncontrolled longitudinal quasi-experimental study in a quantitative research approach was conducted from February to March 2021 among 589 undergraduate nursing students in Tanzania. Following a baseline evaluation, nursing students were enrolled in an interactive web-based clinical practice monitoring system by their program, institution, names, registration numbers, and emails via unique codes created by the lead investigator and trainers. The system recorded and generated feedback on attendance, clinical placement unit, selected or performed clinical nursing procedures, and in-between and end-of-shift feedback. The linear regression was used to assess the effect of the intervention (interactive web-based clinical practice monitoring system) controlled for other correlated factors on motivation in clinical learning (outcome) among nursing students. Nursing students' sociodemographic characteristics and levels of motivation in clinical learning were analyzed descriptively while a 2-tailed paired sample t test established a comparative mean difference in motivation in clinical learning between the pretest and the posttest. The association between variables was determined using regression analysis set at a 95% CI and 5% statistical significance. RESULTS: The mean age of study participants (N=589) was 23 (SD 2.69) years of which 383 (65.0%) were male. The estimated effect (β) of a 3-week intervention to improve nursing students' motivation in clinical learning was 3.041 (P=.03, 95% CI 1.022-7.732) when controlled for other co-related factors. The mean score for motivation in clinical learning increased significantly from the baseline (mean 9.31, SD 2.315) to the postintervention (mean 20.87, SD 5.504), and this improvement presented a large effect size of 2.743 (P<.001, 95% CI 1.011-4.107). CONCLUSIONS: Findings suggest that an interactive web-based clinical practice monitoring system is viable and has the potential to improve undergraduate nursing students' motivation for clinical learning. One alternative clinical pedagogy that educators in nursing education can use to facilitate clinical learning activities and develop motivated undergraduate nursing students is the integration of such technology throughout nursing curricula.
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 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 | 0,006 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».