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Record W2492619187 · doi:10.3233/978-1-61499-658-3-412

Advancing the Digital Health Discourse for Nurse Leaders

2016· article· en· W2492619187 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStudies in health technology and informatics · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Change and Leadership
Canadian institutionsWestern University
Fundersnot available
KeywordsTransformational leadershipeHealthInformaticsHealth informaticsDigital healthNursingHealth careBusinessKnowledge managementPublic relationsMedicinePolitical scienceComputer sciencePublic health

Abstract

fetched live from OpenAlex

Limited informatics competency uptake is a recognized nursing leadership challenge impacting digital practice settings. The health system's inability to reap the promised benefits of EHRs is a manifestation of inadequate development of informatics competencies by chief nurse executives (CNEs) and other clinicians. Through the application of Transformational Leadership Theory (TL), this discussion paper explains how informatics competencies enable CNEs to become transformational nursing leaders in digital health allowing them to meet their accountabilities to lead integrated, high-quality care delivery through evidence based practices (EBPs). It is proposed that successful CNE eHealth sponsors will be those armed with informatics competencies who can drive health organizations' investment in technology and innovation. Finally, some considerations are suggested in how nurse informaticists globally play a critical role in preparing our existing and future CNEs to fulfill their transformational leader roles in the digital age.

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.

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)0.001
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: none
Teacher disagreement score0.915
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.061
GPT teacher head0.347
Teacher spread0.287 · 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