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Record W2811044941 · doi:10.1111/jan.13778

The value of measurement for development of nursing knowledge: Underlying philosophy, contributions and critiques

2018· article· en· W2811044941 on OpenAlexafffund
Pamela Durepos, Elizabeth Orr, Jenny Ploeg, Sharon Kaasalainen

Bibliographic record

VenueJournal of Advanced Nursing · 2018
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMcMaster UniversityHamilton Health Sciences
FundersCanadian Nurses Foundation
KeywordsConstructivePositivismEpistemologyReductionismRealismConstructivism (international relations)Philosophy of scienceNursingNursing Interventions ClassificationPsychologyEngineering ethicsSociologyMedicinePsychological interventionComputer scienceProcess (computing)Philosophy

Abstract

fetched live from OpenAlex

AIM: A philosophical discussion of constructive realism and measurement in the development of nursing knowledge is presented. BACKGROUND: Through Carper's four patterns of knowing, nurses come to know a person holistically. However, measurement as a source for nursing knowledge has been criticized for underlying positivism and reductionist approach to exploring reality. Which seems mal-alignment with person-centred care. DESIGN: Discussion paper. DISCUSSION: Constructive realism bridges positivism and constructivism, facilitating the measurement of physical and psychological phenomena. Reduction of complex phenomena and theoretical constructs into measurable properties is essential to building nursing's empiric knowledge and facilitates (rather than inhibits) person-knowing. IMPLICATIONS FOR NURSING: Nurses should consider constructive realism as a philosophy to underpin their practice. This philosophy supports measurement as a primary method of inquiry in nursing research and clinical practice. Nurses can carefully select, and purposefully integrate, measurement tools with other methods of inquiry (such as qualitative research methods) to demonstrate the usefulness of nursing interventions and highlight nursing as a science.

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)0.001
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 score0.880
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.093
GPT teacher head0.426
Teacher spread0.333 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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