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Record W2067255143 · doi:10.1002/nur.10063

Ethological methods to develop nursing knowledge

2003· review· en· W2067255143 on OpenAlexafffund
Fay Warnock, Marion Allen

Bibliographic record

VenueResearch in Nursing & Health · 2003
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersMedical Research CouncilCanadian Institutes of Health ResearchMedical Research Council Canada
KeywordsEthologyPsychologyPhenomenonNursing theoryDescriptive researchArgument (complex analysis)NursingEpistemologyMEDLINEMedicineSociologyEcologySocial sciencePhilosophyBiology

Abstract

fetched live from OpenAlex

Researchers from various fields use ethological methods to systematically observe, describe, and measure animal and human nonverbal behavior. The purpose of this article is to argue that their application in nursing will benefit development of descriptive-level knowledge about complex behavioral phenomena. To advance the argument for applying these methods in nursing, we examine the compatibility of the philosophical assumptions underlying ethology with nursing, assess if ethology can help nursing achieve some of its aims, and determine the benefits of using ethology when observation of a phenomenon is required. Neonatal pain is used to illustrate how ethology can be used to develop descriptive-level nursing knowledge and midrange theory.

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)0.040
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0040.010
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.500
GPT teacher head0.692
Teacher spread0.191 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2003
Admission routes2
Has abstractyes

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