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Record W3126130954 · doi:10.7202/1074102ar

Évaluer le transfert des compétences infirmières : analyse des effets d’une formation en soins intermédiaires en milieu hospitalier

2020· article· fr· W3126130954 on OpenAlexaffvenue
Yves Chochard, Jenny Gentizon, Serge Gallant

Bibliographic record

VenueMesure et évaluation en éducation · 2020
Typearticle
Languagefr
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cette recherche porte sur l’évaluation de l’efficacité d’une formation en milieu hospitalier à partir d’indicateurs de taille d’effet et d’indice d’hétérogénéité. L’évaluation portait sur une formation en soins intermédiaires destinée aux infirmières et infirmiers d’une durée de 23 jours et qui intégrait notamment trois jours d’enseignement clinique au chevet du patient. Les compétences ont été mesurées en début et en fin de formation, à partir d’une grille d’observation standardisée basée sur les rôles d’expert clinique, de communicateur, de collaborateur, de manager et d’apprenant-formateur. Les estimateurs de Cohen et de Glass ont démontré des effets significatifs de la formation sur les cinq domaines, tandis que l’indice d’hétérogénéité a mis en évidence une réduction des disparités de comportements au sein du groupe au terme de la formation. La discussion aborde la question des balises utilisées pour interpréter les tailles d’effet.

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)0.020
metaresearch head score (Gemma)0.060
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.060
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.096
GPT teacher head0.386
Teacher spread0.289 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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