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Expert decision making in relation to unanticipated blood glucose levels

2000· article· en· W4235007303 on OpenAlexaff
Barbara Paterson, Sally Thorne

Bibliographic record

VenueResearch in Nursing & Health · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConstruct (python library)PsychologyDecision fatigueAction (physics)Social psychologyClinical decision makingRelation (database)Process (computing)Focus (optics)Cognitive psychologyR-CASTBusiness decision mappingApplied psychologyMedicineDecision support systemComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

How people (N = 22) with long-standing Type I diabetes make everyday self-care decisions, specifically in regard to unanticipated blood glucose levels (UBGLs) was investigated using grounded theory. Participants differentiated between decisions made in familiar and typical situations and those made in novel situations. Decisions made in familiar situations were straightforward, arising from a confident appraisal of the cause of the UBGL. The primary focus of decision making in response to an UBGL in familiar situations was the decision about the course of action. The focus in unfamiliar situations was the appraisal of the cause of the UBGL. It was characterized by the participants' lack of confidence and by a non-linear progression in which the individual retraced previous phases of the decision-making process or proceeded to tangential steps. Participants matched the features of previously encountered situations to construct a story that explained the events in order to generate some plausible hypotheses. A number of contextual and mediating variables were identified as influencing the decision-making process and the decisions they made. The findings of this research demonstrate that the decision maker's familiarity with the situation influences the nature of the decision-making processes that are used. © 2000 John Wiley & Sons, Inc. Res Nurs Health 23:147–157, 2000

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)0.000
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: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.088
GPT teacher head0.475
Teacher spread0.387 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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