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Record W2981492866 · doi:10.1097/nna.0000000000000810

The Role of Nurse Managers in the Adoption of Health Information Technology

2019· article· en· W2981492866 on OpenAlexaffabout
Gillian Strudwick, Richard Booth, Ragnhildur I. Bjarnadóttir, Sarah Collins Rossetti, Madison Friesen, Lydia Sequeira, Mikayla Munnery, Rani Srivastava

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2019
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsToronto Public HealthCentre for Addiction and Mental HealthWestern University
Fundersnot available
KeywordsNursingQualitative researchHealth careVariety (cybernetics)FacilitationFocus groupPsychologyMedicineBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aims to investigate the role of nurse managers in supporting point-of-care nurses' health information technology (IT) use and identify strategies employed by nurse managers to improve adoption, while also gathering point-of-care nurses' perceptions of these strategies. BACKGROUND: Nurse managers are essential in facilitating point-of-care nurses' use of health IT; however, the underlying phenomenon for this facilitation remains unreported. METHODS: A qualitative descriptive study was conducted with 10 nurse managers and 14 point-of-care nurses recruited from a mental health hospital environment in Ontario, Canada. Inductive and deductive content analyses were used to analyze the semistructured interviews. RESULTS: Nurse managers adopt the role of advocate, educator, and connector, using the following strategies: communicating system updates, demonstrating use of health IT, linking staff to resources, facilitating education, and providing IT oversight. CONCLUSIONS: Nurse managers use a variety of strategies to support nurses' use of health IT. Future research should focus on the effectiveness of these strategies.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.416
Teacher spread0.387 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations19
Published2019
Admission routes2
Has abstractyes

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