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Nursing Intellectual Capital Theory: Implications for Research and Practice

2013· article· en· W83686211 on OpenAlex

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

VenueOJIN The Online Journal of Issues in Nursing · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIntellectual capitalNursingNursing theoryNursing researchPsychologyQuality (philosophy)Health careEmpirical researchKnowledge managementBusinessMedicineMEDLINEPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Due to rising costs of healthcare, determining how registered nurses and knowledge resources influence the quality of patient care is critical. Studies that have investigated the relationship between nursing knowledge and outcomes have been plagued with conceptual and methodological issues. This has resulted in limited empirical evidence of the impact of nursing knowledge on patient or organizational outcomes. The nursing intellectual capital theory was developed to assist with this area of inquiry. Nursing intellectual capital theory conceptualizes the sources of nursing knowledge available within an organization and delineates its relationship to patient and organizational outcomes. In this article, we review the nursing intellectual capital theory and discuss its implications for research and practice. We explain why the theory shows promise for guiding research on quality work environments and how it may assist with administrative decision-making related to nursing human resource management and continuing professional development.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.066
GPT teacher head0.409
Teacher spread0.343 · 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