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Record W1520223695 · doi:10.1002/nur.21596

Organizational Climate and Hospital Nurses' Caring Practices: A Mixed‐Methods Study

2014· article· en· W1520223695 on OpenAlexafffund
Geneviève Roch, Carl‐Ardy Dubois, Sean P. Clarke

Bibliographic record

VenueResearch in Nursing & Health · 2014
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMcGill UniversityUniversité de MontréalUniversité LavalThe Quebec Population Health Research Network
FundersCanadian Institutes of Health ResearchHealth CanadaCanadian Health Services Research Foundation
KeywordsNursingWorkloadOrganisation climatePsychological interventionQualitative researchOrganizational cultureHealth careAmbiguityMedicinePsychologyPublic relationsSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Organizational climate in healthcare settings influences patient outcomes, but its effect on nursing care delivery remains poorly understood. In this mixed-methods study, nurse surveys (N = 292) were combined with a qualitative case study of 15 direct-care registered nurses (RNs), nursing personnel, and managers. Organizational climate explained 11% of the variation in RNs' reported frequency of caring practices. Qualitative data suggested that caring practices were affected by the interplay of organizational climate dimensions with patients and nurses characteristics. Workload intensity and role ambiguity led RNs to leave many caring practices to practical nurses and assistive personnel. Systemic interventions are needed to improve organizational climate and to support RNs' involvement in a full range of caring practices.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.544
Teacher spread0.458 · 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 designObservational
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

Citations71
Published2014
Admission routes2
Has abstractyes

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