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Enregistrement W2792504100 · doi:10.18438/eblip29385

Nurses Need Training and Policies to Address Barriers to Use of Mobile Devices and Apps for Direct Patient Care in Hospital Settings

2018· article· en· W2792504100 sur OpenAlexaffvenueabout
Kelley Wadson

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2018
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensBow Valley College
Organismes subventionnairesnon disponible
Mots-clésIntervention (counseling)Health careNursingSession (web analytics)Descriptive statisticsMedicineFamily medicineThe InternetPsychologyMedical educationPolitical science

Résumé

récupéré en direct d'OpenAlex

A Review of: Giles-Smith, L., Spencer, A., Shaw, C., Porter, C., & Lobchuk, M. (2017). A study of the impact of an educational intervention on nurse attitudes and behaviours toward mobile device and application use in hospital settings. Journal of the Canadian Health Libraries Association/Journal de l'Association des bibliothèques de la santé du Canada, 38(1), 12-29. doi: 10.5596/c17-003 Abstract Objective - To describe nurses’ usage of and attitudes toward mobile devices and apps and assess the impact of an educational intervention by hospital librarians and educators Design - Descriptive, cross-sectional survey, one-group pre- and post-test, and post-intervention focus group Setting - One 251-bed community hospital and one 554-bed tertiary care hospital in Winnipeg, Canada Subjects - 348 inpatient medical and surgical nurses Methods – The study had two phases. In Phase I, respondents completed a survey of 21 fixed and open-ended questions offered online or in print to a convenience sample from the community hospital and a random sample of medical and surgical units from the tertiary hospital. The survey collected demographic data and included questions about mobile devices and apps covering current awareness of hospital policy, ownership, internet access, usage patterns, concerns, and attitudes toward their use for direct patient care. It also included information to recruit volunteers for Phase II. In Phase II, participants attended four 30-minute educational sessions facilitated by the researchers. The first session addressed the regional health authority’s policies, Personal Health Information Act, and infection control practices. Subsequent sessions covered relevance, features, and training exercises for one or more selected apps. Participants installed five free or low-cost apps, which were chosen by the librarians and nurse educators, on their mobile devices: Medscape, Lab Tests Online, Lexicomp, Twitter, and Evernote. Participants were then given a two-month period to use the apps for patient care. Afterward, they completed the same survey from Phase I and their pre- and post-intervention responses were matched for comparative analysis. Phase II concluded with a one-hour audio-recorded focus group using ten open-ended questions to gather feedback on the impact of the educational sessions. Main Results – 94 nurses completed the Phase I survey for a response rate of 27%. Although 89 respondents reported owning a mobile device, less than half used them for patient care. Just under half the respondents were unsure if they were allowed to use mobile devices at work and a similar number answered that devices were not allowed. Two-thirds of respondents were unsure whether any institutional policies existed regarding mobile device use. Of the 16 participants that volunteered for Phase II, 14 completed the post-intervention survey and 6 attended the focus group. In comparison to the Phase I survey, post-intervention survey responses showed more awareness of institutional policies and increased concern about mobile devices causing distraction. In the Phase I survey, just over half of the nurses expressed a desire to use mobile devices in patient care. Four themes emerged from the survey’s qualitative responses in Phase I: (1) policy: nurses were unsure of institutional policy or experienced either disapproval or bans on mobile device use from management; (2) barriers to use, namely cost, potential damage to or loss of devices, infection control, and lack of familiarity with technology; (3) patient perceptions, including generational differences with younger patients seen as more accepting than older patients; and (4) nurse perceptions: most valued access to information but expressed concerns about distraction, undermining of professionalism, and use of technology. Qualitative responses in the Phase II survey and focus group also revealed four themes: (1) barriers: participants did not cite loss of device or infection control as concerns as in Phase I; (2) patient acceptance and non-acceptance: education and familiarity with mobile devices were noted as positive influential factors; (3) information need, accessibility, and convenience: nurses reported needing easy-to-use apps, particularly Lexicomp, and appreciated improved access to information; and (4) nurse behaviour and attitude: participants reported more time would be needed for changes to occur in these areas. Conclusion – The study found that although most nurses own mobile devices and express strong interest in using them for patient care, there are significant barriers including lack of clarity about institutional policies and concerns about infection control, risk of damage to personal devices, costs, lack of experience with the technology, distraction, and negative patient perceptions. To address these concerns, the authors recommend that hospital librarians and educators work together to offer training and advocate for improved communication and policies regarding use of mobile devices in hospital settings. Moreover, the study affirmed the benefits of using mobile devices and apps to support evidence-based practice, for example by providing access to reliable drug information. The authors conclude that additional research is needed to inform policy and develop strategies that hospital librarians and nurse educators can use to promote the most effective application of mobile technologies for patient care.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,056
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,081

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,056
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,002
Communication savante0,0050,005
Science ouverte0,0020,003
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0110,003

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 Opus0,027
Tête enseignante GPT0,374
Écart entre enseignants0,346 · 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 source (Gemma direct ou Codex distillé), 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

Citations1
Publié2018
Routes d'admission3
Résumé présentoui

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