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Record W1992278198 · doi:10.3928/01484834-20080801-02

Decision Making by Baccalaureate Nursing Students in the Clinical Setting

2008· article· en· W1992278198 on OpenAlexaff
Pamela Baxter, Sheryl Boblin

Bibliographic record

VenueJournal of Nursing Education · 2008
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInterdependenceClinical decision makingNurse educatorPsychologyBaccalaureate DegreeMedical educationNursingProcess (computing)Nurse educationQualitative researchMedicineHigher educationComputer scienceFamily medicineSociology

Abstract

fetched live from OpenAlex

Many researchers who have explored nurse decision making have concluded that decision making is a learned skill that must be taught by nurse educators. Yet little research has been conducted to explore nursing students' decision making. If nurse educators are to teach this skill, it is necessary to have a better understanding of the kinds of decisions students are making in the clinical setting and the factors that influence this process. Once we have a greater knowledge in this area, curricular materials can be developed to ensure this skill is taught throughout an undergraduate education, resulting in graduates who possess strong, independent, and interdependent decision making skills. This article will describe one component (the kinds of decisions) of a larger qualitative case study that explored the kinds of decisions and the factors that influenced nursing students' decision making throughout a baccalaureate degree program.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0090.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.475
Teacher spread0.415 · 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 source (direct Gemma or distilled Codex), 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

Citations45
Published2008
Admission routes1
Has abstractyes

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