Use of Health Apps by Nurses for Professional Purposes: Web-Based Survey Study
Notice bibliographique
Résumé
BACKGROUND: In the last few years, the number of mobile apps for health professionals has increased exponentially. Nevertheless, there is a lack of knowledge about the professional use, training requirements, and quality perception of these apps among health care professionals such as nurses. Considering that the nursing profession is the largest segment of health care workforce in many countries such as Spain, the impact of the use of health apps by these professionals can be critical to the future of modern health care. OBJECTIVE: The main objective of this study was to determine if nurses were using health apps professionally and what types of apps they were using. The secondary objectives were (1) to find out if, among nurses, there is a need for training in the use of health apps and (2) to explore nurses' perceptions of health professional apps, determining whether there is a need for a certification process for health apps and the type of institution or organization that should review and validate these apps for professional use. METHODS: After an initial piloting survey, all registered nurses at the Nursing Association of Barcelona were invited to participate in a 34-item online survey. Eventually, 1293 nurses participated in the survey; however, 52 did not complete the survey properly, omitting both age or gender information, and they were excluded from the analysis. RESULTS: About half of the respondents (600/1241, 48.35%) had health professional apps installed on their devices and were included for analysis. Most participants in the survey were women (474/600, 79.0%) and the remaining were men (126/600, 21.0%). The most popular types of apps used and installed among nurses were related to drug information, health calculators, and health guidelines. Overall, 97.0% (582/600) of nurses thought that the health apps should be certified, and 80.0% (480/600) agreed that the certification process should be carried out by professional or health institutions. Furthermore, 14.5% (87/600) of participants mentioned that they were asked by their patients to prescribe a health app and only 6.5% (28/430) recommended them often. Most nurses (354/433, 81.8%) who answered the question about the importance of receiving specific training on using and prescribing health apps considered this point a very relevant issue. CONCLUSIONS: About half of the nurses in Catalonia use health apps for professional purposes, and they believe that these types of tools should be validated and certified by health or professional institutions before using them in clinical environments. Although the prescription of health apps in clinical environments is infrequent among nurses, they would be willing to prescribe apps if they were certified by a health organization. Finally, among nurses, there is a need for training in using and prescribing health apps for health care purposes.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».