MétaCan
Menu
Back to cohort
Record 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 on OpenAlexaffvenueabout
Kelley Wadson

Bibliographic record

VenueEvidence Based Library and Information Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsBow Valley College
Fundersnot available
KeywordsIntervention (counseling)Health careNursingSession (web analytics)Descriptive statisticsMedicineFamily medicineThe InternetPsychologyMedical educationPolitical science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.024
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.374
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueEvidence Based Library and Information PracticeSame topicMobile Health and mHealth ApplicationsFrench-language works237,207