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Record W3081762009 · doi:10.1002/nop2.608

Comparing feeling of competence regarding humanistic caring in Belgian nurses and nursing students: A comparative cross‐sectional study conducted in a French Belgian teaching hospital

2020· article· en· W3081762009 on OpenAlexaff
Dan Lecocq, Philippe Delmas, Matteo Antonini, Hélène Lefebvre, Martine Laloux, Amélie Beghuin, Chantal Van Cutsem, Aurélia Bustillo

Bibliographic record

VenueNursing Open · 2020
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFeelingCompetence (human resources)Cross-sectional studyNursingHumanismPsychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Aim: The aim of the study was to describe and compare feeling of competence regarding humanistic caring in Registered Nurses (RN) and nursing students (NS). Design: A quantitative comparative cross-sectional research design was used. Methods: A convenience sample of 196 RN and 47 NS in a teaching hospital in Belgium completed a self-administered questionnaire composed of a sociodemographic survey and the Caring Nurse-Patient Interactions Scale (CNPI-23) developed by Cossette et al. Results: The four dimensions of the CNPI-23 were compared using the Skillings-Mack test. Both groups scored higher on "humanistic" and "comforting" than on "clinical" and "relational" care and both scored lowest on this last dimension. Linear regressions showed that none of the variables had a statistically significant influence on the CNPI-23 scores, except for NS "state of health," which influenced their feeling of competence regarding "relational care."

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)0.002
metaresearch head score (Gemma)0.002
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.103
GPT teacher head0.443
Teacher spread0.340 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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