Nurses as Stakeholders in the Adoption of Mobile Technology in Australian Health Care Environments: Interview Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND The 2017 Australian Digital Health Agency (ADHA) Strategy is based on the underlying assumption that digital technology in health care environments is ubiquitous. The ADHA Strategy views health professionals, especially nurses, as grappling with the complexity of installing and using digital technologies to facilitate personalized and sustainable person-centered care. Yet, ironically, the 2018 debate over how to enroll Australians into the national electronic health record system and its alteration from an opt-in to an opt-out model heightened public and professional concern over what constituted a “safe, seamless and secure” health information system. What can be termed a digital technology paradox has emerged where, although it is widely acknowledged that there are benefits from deploying and using digital technology in the workplace, the perception of risk renders it unavailable or inaccessible at point of care. The inability of nurses to legitimately access and use mobile technology is impeding the diffusion of digital technology in Australian health care environments and undermining the 2017 ADHA Strategy. OBJECTIVE This study explored the nature and scope of usability of mobile technology at point of care, in order to understand how current governance structures impacted on access and use of digital technology from an organizational perspective. METHODS Individual semistructured interviews were conducted with 6 representatives from professional nursing organizations. A total of 10 interview questions focused on factors that impacted the use of mobile technology for learning at point of care. Seven national organizations and 52 members from the Coalition of National Nursing and Midwifery Organisations were invited to participate. Interviews were recorded and transcribed verbatim. Data analysis was systematic and organized, consisting of trial coding; member checking was undertaken to ensure rigor. A codebook was developed to provide a framework for analysis to identify the themes latent in the transcribed data. Nurses as stakeholders emerged as a key theme. RESULTS Out of 6 participants, 4 female (67%) and 2 male (33%) senior members of the nursing profession were interviewed. Each interview lasted between 17 and 54 minutes, which reflected the knowledge of participants regarding the topic of interest and their availability. Two subthemes, coded as ways of thinking and ways of acting, emerged from the open codes. Participants provided examples of the factors that impacted the capacity of nurses to adopt digital technology from an emic perspective. There were contributing factors that related to actions, including work-arounds, attentiveness, and experiences. Nurses also indicated that there were attitudes and influences that impacted thinking regarding access and use of mobile technology at point of care. CONCLUSIONS Nurses are inadequately prepared for the digital future that has now arrived in health care environments. Nurses do not perceive that they are leaders in decision making regarding digital technology adoption, nor are they able to facilitate digital literacy or model digital professionalism.
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 | 0,008 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».