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Readiness of Nurse Executives and Leaders to Advocate for Health Information Systems Supporting Nursing

2015· article· en· W179430670 on OpenAlexaffabout
Mary Oakes, Noreen Frisch, Pamela J. Potter, Elizabeth M. Borycki

Bibliographic record

VenueStudies in health technology and informatics · 2015
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNursingHealth informaticsInformaticsCurriculumUnit (ring theory)Nursing researchHealth careNurse educationNursing practiceMedicinePsychologyMedical educationPolitical sciencePublic healthPedagogy

Abstract

fetched live from OpenAlex

Literature suggests that nurses at all levels remain unprepared to make use of full use of technologies required for current practice. Specifically, nurses lack sufficient background to engage with information systems, electronic records, and unit-level and aggregated data that could support understanding of the contributions of nursing services to care and the development of best practice guidelines. Given that nursing leadership is needed in this area, the authors conducted a review of graduate nursing curricula preparing nurses to take on leadership, management and executive roles. Findings revealed that only 12 out of 36 graduate nursing programs on the west coast of the US and Canada required any content in informatics, and five more offered elective opportunities. Implications for education and practice are discussed.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.074
GPT teacher head0.448
Teacher spread0.374 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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