Effects of E-Learning in a Continuing Education Context on Nursing Care: Systematic Review of Systematic Qualitative, Quantitative, and Mixed-Studies Reviews
Notice bibliographique
Résumé
BACKGROUND: E-learning is rapidly growing as an alternative way of delivering education in nursing. Two contexts regarding the use of e-learning in nursing are discussed in the literature: (1) education among nursing students and (2) nurses' continuing education within a life-long learning perspective. A systematic review of systematic reviews on e-learning for nursing and health professional students in an academic context has been published previously; however, no such review exists regarding e-learning for registered nurses in a continuing education context. OBJECTIVE: We aimed to systematically summarize the qualitative and quantitative evidence regarding the effects of e-learning on nursing care among nurses in a continuing education context. METHODS: We conducted a systematic review of systematic qualitative, quantitative, and mixed-studies reviews, searching within four bibliographic databases. The eligibility criteria were formulated using the population, interventions, comparisons, outcomes, and study design (PICOS) format. The included population was registered nurses. E-learning interventions were included and compared with face-to-face and any other e-learning interventions, as well as blended learning. The outcomes of interest were derived from two models: nursing-sensitive indicators from the Nursing Care Performance Framework (eg, teaching and collaboration) and the levels of evaluation from the Kirkpatrick model (ie, reaction, learning, behavior, and results). RESULTS: We identified a total of 12,906 records. We retrieved 222 full-text papers for detailed evaluation, from which 22 systematic reviews published between 2008 and 2018 met the eligibility criteria. The effects of e-learning on nursing care were grouped under Kirkpatrick's levels of evaluation: (1) nurse reactions to e-learning, (2) nurse learning, (3) behavior, and (4) results. Level 2, nurse learning, was divided into three subthemes: knowledge, skills, attitude and self-efficacy. Level 4, results, was divided into patient outcomes and costs. Most of the outcomes were reported in a positive way. For instance, nurses were satisfied with the use of e-learning and they improved their knowledge. The most common topics covered by the e-learning interventions were medication calculation, preparation, and administration. CONCLUSIONS: The effects of e-learning are mainly reported in terms of nurse reactions, knowledge, and skills (ie, the first two levels of the Kirkpatrick model). The effectiveness of e-learning interventions for nurses in a continuing education context remains unknown regarding how the learning can be transferred to change practice and affect patient outcomes. Further scientific, methodological, theoretical, and practice-based breakthroughs are needed in the fast-growing field of e-learning in nursing education, especially in a life-learning perspective. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42016050714; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=50714.
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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 | 0,052 | 0,187 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,011 | 0,009 |
| Bibliométrie | 0,028 | 0,025 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».