MétaCan
Menu
Retour à la cohorte
Enregistrement W4392816803 · doi:10.5430/jnep.v14n6p22

What are the experiences of student nurses with online learning? Do they have the necessary digital and technological competencies?

2024· article· en· W4392816803 sur OpenAlexvenueno aff
Christina Ebanks

Notice bibliographique

RevueJournal of Nursing Education and Practice · 2024
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueE-Learning and COVID-19
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyOnline learningMedical educationNursingComputer scienceMedicineMultimedia

Résumé

récupéré en direct d'OpenAlex

Background and objective: Student Nurses have been conventionally and predominantly taught face to face for several decades. A recent surge in teaching student nurses online in the last decade has been expedited by the onset of the covid-19 pandemic. A significant number of research on online learning, focuses on its effectiveness from an educator’s perspective. Exploring student nurses’ experiences with online learning in relation to their digital and technology skills readiness is pertinent to informing a student-led pedagogy. The research aims to explore the experiences of student nurses with online learning and if they are digital and technology skills ready for online learning or not.Methods: The study is a descriptive qualitative research, which utilises Interpretative phenomenological analysis and hermeneutic Interpretative phenomenology. Four pre-registration student nurses in a university in the South-East of England were recruited for the study. Individual face-to-face tape recorded semi-structured interviews were conducted with verbal and written consent from participants. Data collected was concurrently transcribed and analysed. Preliminary codes were given to the collected data to describe the contents. Interviews were then searched for patterns in the given codes from the transcripts. The themes that emerged were reviewed and refined with written up verbatim quotations from participants to support interpretations. A reflexive diary was kept by the researcher throughout the research, to reduce the likelihood of biases. Results: The themes that emerged from the collected and analysed data indicated that student nurses were digital and technology skills competent to engage in online learning. Online learning was deemed beneficial by all students although a preference for face-to-face learning was reported. Factors that inhibited students from fully engaging with online learning included internet hitches and the inability of nurse educators to use technology. A lack of effective communication between lecturers and students during online learning also marred the experiences of students. Environmental distractions at home and a lack of support from peers and lecturers during online learning were further cited as inhibitors for online learning.Conclusions: The Nursing and Midwifery Council (NMC) requires qualified nurses to have sound technology skills for care delivery. Considerations for online learning must include a prior technology skills competence assessment. The approach will ensure a level playing field for all students who engage in online learning. The appropriate support and interventions can be put in place for students who may not have the prerequisite level of technology skills to engage in online learning. Findings supports a blended learning approach with a student led digital and technology skills baseline assessment, prior to online learning. The approach will ensure a successful co-creation with an amalgamation with pedagogy.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,186
Score d'incertitude au seuil0,927

Scores Codex et Gemma par catégorie

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

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 Opus0,062
Tête enseignante GPT0,441
Écart entre enseignants0,379 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
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

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Nursing Education and PracticeMême sujetE-Learning and COVID-19Travaux en français237 207