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Differences and similarities in the perception of caring between Spanish and UK nurses

2002· article· en· W2094828942 on OpenAlexaff
Roger Watson, Amandah Lea Hoogbruin, Carmen Rumeu, Maribel Beunza, Beatriz Barbarin, Julie MacDonald, Tracey McCready

Bibliographic record

VenueJournal of Clinical Nursing · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsPerceptionDimension (graph theory)PsychologyNursingContrast (vision)Work (physics)EmpathySocial psychologyMedicine

Abstract

fetched live from OpenAlex

The aim of the present paper was to compare and contrast perceptions of caring in nursing between Spanish and UK nurses. There are no previous studies comparing directly the perceptions of caring across cultures in nursing. A survey method was used employing the 25-item Caring Dimensions Inventory. Data were Mokken scaled for comparison with data from a previous study and scores for common items on the 25-item Caring Dimension Inventory for Spanish and UK nurses were correlated. There were similarities and differences between Spanish and UK nurses' perceptions of caring: many similar items were incorporated into Mokken scales but the endorsement of items did not correlate. The present work demonstrates that it is possible to measure differences and similarities in perceptions of caring. The study design could be improved and such work could be valuable in cross-cultural work with nurses.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.169
GPT teacher head0.460
Teacher spread0.292 · 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 designQualitative
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

Citations38
Published2002
Admission routes1
Has abstractyes

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