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Record W3210983020 · doi:10.3917/rsi.146.0044

Effet d’un club de lecture sur le sentiment d’efficacité personnelle, les attentes et les intentions d’étudiantes en sciences infirmières à l’égard de l’utilisation des résultats probants

2021· article· fr· W3210983020 on OpenAlexaff
Caroline Gibbons, Jimmy Bourque, Tim Aubry

Bibliographic record

VenueRecherche en soins infirmiers · 2021
Typearticle
Languagefr
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of OttawaRoyal College of Physicians and Surgeons of CanadaUniversité de Moncton
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

INTRODUCTION: Nursing associations require that nurses develop the skills to integrate evidence into practice to support the quality of care. CONTEXT: Lack of self-confidence in the operational steps of evidence-based practice was identified as a barrier to integrating evidence into nursing practice. OBJECTIVE: To assess the effect of a journal club (JC) on nursing students' self-efficacy (SE), expectations, and intentions to use evidence. METHOD: Quasi-experimental, longitudinal approach with a non-randomized comparison group. RESULTS: The development of SE toward the use of evidence-based practices favored students who participated in the JC (n=48) compared to students who received a conventional educational modality (CEM) (n=50). However, there was no significant group x time interaction effect on expectations or intentions. DISCUSSION: The significant positive changes beyond the CEM may be explained by the fact that the JC incorporated modes of influence on SE. CONCLUSION: The positive effect associated with the JC on SE is difficult to sustain without continued practice. It is important to maintain high outcome expectations within nursing training.

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)0.007
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.002

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 head0.344
GPT teacher head0.498
Teacher spread0.155 · 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.

Study designObservational
DomainMethods
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

Citations2
Published2021
Admission routes1
Has abstractyes

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