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Record W3025073017

Past, Present and Future: The Outlook from Mid-Career Nurse Informaticians

2016· article· en· W3025073017 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

VenueElectronicHealthcare · 2016
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsOntario HIV Treatment NetworkSciencetech (Canada)
Fundersnot available
KeywordsMainstreamLeverage (statistics)ChoseNursingHealth informaticsMedicinePublic relationsMedical educationPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Nursing informatics (NI), as a specialty of nursing, can trace its origins back as far as the 1960s. While difficult to find empirical evidence to pinpoint exactly when NI moved from the fringes of nursing to more mainstream recognition, the late 1990s to early 2000s was a period of significant growth in the desire to leverage information technology as a means of collecting more robust and reliable healthcare information. This, in turn, has led to a significant increase in the number of nurses working as NI specialists. Those who have remained in NI roles since this time are now reaching the point. This paper will examine the current NI landscape and the experience of a number of early and mid-career nurses who chose to focus on NI by exploring how and why they chose this career path, the opportunities and challenges they have faced to date and their predictions for the future of NI.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.279
Teacher spread0.268 · 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