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Thinking Processes Used by Nurses in Clinical Decision Making

2002· article· en· W85355206 on OpenAlex
Kathryn A. Smith Higuchi, Janet G. Donald

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

VenueJournal of Nursing Education · 2002
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsClinical decision makingVariety (cybernetics)VocabularyContext (archaeology)Clinical PracticeNursing processCritical thinkingPsychologyNursingManagement scienceMedical educationMedicineComputer sciencePedagogyFamily medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Clinical decision making forms the basis of expert clinical practice. The purpose of this study was to investigate and document the thinking processes used by nurses in clinical decision making situations so the processes could guide educational practice. Clinical data was analyzed to reveal that clinical decision making is complex and requires a variety of thinking processes. Medical and surgical nurses used different thinking processes, showing the importance of context in clinical decision making. The nursing exemplars and working vocabulary developed in this study to describe the thinking processes used in clinical decision making can be used in nursing education.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.179
GPT teacher head0.558
Teacher spread0.379 · 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