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Record W2601601346 · doi:10.3233/978-1-61499-738-2-41

Curricula Challenges and Informatics Competencies for Nurse Educators

2017· article· en· W2601601346 on OpenAlex
Ulla‐Mari Kinnunen, Elina Rajalahti, Elizabeth Cummings, Elizabeth M. Borycki

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 · 2017
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInformaticsHealth informaticsHealth Administration InformaticsNursingCurriculumMedical educationNurse educationBusiness informaticsEngineering informaticsMedicinePsychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

Nursing informatics competencies are fundamental to nursing practice in all areas of nursing work, including direct patient care, administration and education. The recent activity relating to the development of nursing informatics competencies for beginning level nurses has exposed a paucity of understanding of the requirements for nursing informatics competencies for nurse educators. So, whilst the challenge of educating faculty to teach informatics has been limited, research into such competencies is required to meet this challenge. This paper describes the challenges and issues associated with nursing informatics competency development for faculty, outlines the capabilities of faculty, and presents a vision for the future of informatics education for faculty. The final requirement of the introduction of new competencies is to determine appropriate evaluation measures that reflect the requirements of all stakeholders.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
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 head0.140
GPT teacher head0.500
Teacher spread0.360 · 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