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Record W2763000923 · doi:10.1136/bmjopen-2017-018129

Exploring the role of the nurse manager in supporting point-of-care nurses’ adoption of electronic health records: protocol for a qualitative research study

2017· article· en· W2763000923 on OpenAlexafffundabout
Gillian Strudwick, Richard Booth, Ragnhildur I. Bjarnadóttir, Sarah Collins, Rani Srivastava

Bibliographic record

VenueBMJ Open · 2017
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsWestern UniversityCentre for Addiction and Mental Health
FundersRegistered Nurses' Association of Ontario
KeywordsMedicineQualitative researchNursingProtocol (science)Health careContent analysisAlternative medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: An increasing number of electronic health record (EHR) systems have been implemented in clinical practice environments where nurses work. Findings from previous studies have found that a number of intended benefits of the technology have not yet been realised to date, partially due to poor system adoption among health professionals such as nurses. Previous studies have suggested that nurse managers can support the effective adoption and use of the technology by nurses. However, no known studies have identified what role nurse managers have in supporting technology adoption, nor the specific strategies that managers can employ to support their staff. Therefore, the purpose of this research is to better understand the role of the nurse manager in point-of-care nurses' use of EHRs, and to identify strategies that may be effective in supporting clinical adoption. METHODS AND ANALYSIS: This study will use a qualitative descriptive design. Interviews with both nurse managers and point-of-care nursing staff will be conducted in a Canadian mental health and addiction healthcare organisation where an EHR has been implemented. A semistructured interview guide will be used, and interviews will be audio recorded. Transcripts will be analysed using a directed content analysis technique. Strategies to ensure the trustworthiness of the data analysis procedure and findings will be employed. ETHICS AND DISSEMINATION: Ethical approval for this study has been obtained. Dissemination strategies may include a paper submission to a peer-reviewed journal, a conference submission and meetings to share findings with the study site leadership team. Findings from this research will be used to inform a future study which aims to assess levels of competencies and perform a psychometric analysis of the Nursing Informatics Competency Assessment for the Nurse Leader instrument in a Canadian context.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.095
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.095
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.072
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.005
Science and technology studies0.0090.006
Scholarly communication0.0060.005
Open science0.0060.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0420.009

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 head0.540
GPT teacher head0.689
Teacher spread0.149 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations8
Published2017
Admission routes3
Has abstractyes

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