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Record W2443325406 · doi:10.3233/978-1-61499-738-2-119

Competency Recommendations for Advancing Nursing Informatics in the Next Decade: International Survey Results

2017· article· en· W2443325406 on OpenAlexaff
Charlene Ronquillo, Maxim Topaz, Lisiane Pruinelli, Laura‐Maria Peltonen, Raji Nibber

Bibliographic record

VenueStudies in health technology and informatics · 2017
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInformaticsThematic analysisHealth informaticsNursingService (business)Medical educationHealth Administration InformaticsNurse educationVisibilityMedicineQualitative researchPolitical scienceSociologyBusinessGeography

Abstract

fetched live from OpenAlex

The IMIA-NIstudents' and emerging professionals' working group conducted a large international survey in 2015 regarding research trends in nursing informatics. The survey was translated into half-a-dozen languages and distributed through 18 international research collaborators' professional connections. The survey focused on the perspectives of nurse informaticians. A total of 272 participants responded to an open ended question concerning recommendations to advance nursing informatics. Five key areas for action were identified through our thematic content analysis: education, research, practice, visibility and collaboration. This chapter discusses these results with implications for nursing competency development. We propose how components of various competency lists might support the key areas for action. We also identify room to further develop existing competency guidelines to support in-service education for practicing nurses, promote nursing informatics visibility, or improve and facilitate collaboration and integration with other professions.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.127
GPT teacher head0.472
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.

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

Citations9
Published2017
Admission routes1
Has abstractyes

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