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Record W4417075750 · doi:10.15173/cjsc.v1i1.3971

Rester CALME en Partenariat : Orienter les Conversations Difficiles Entre Chercheurs et Partenaires Familiaux.

2025· article· W4417075750 on OpenAlexaff
Tanya Chute-Nagy, Clara Jordan, Martine Couture

Bibliographic record

VenueThe Canadian Journal of Science Communication · 2025
Typearticle
Language
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsResearch methodologyContext (archaeology)

Abstract

fetched live from OpenAlex

Impliquer les personnes ayant une expérience vécue, celles directement touchées par une condition ou prenant soin d’une personne concernée, est de plus en plus courant dans les recherches axées sur leur expertise.1 Les familles peuvent y participer par le biais d’un partenariat, ce qui signifie une implication active et importante en tant que membres de l’équipe de recherche. Dans la recherche en santé, cette approche renforce le lien des familles avec les soins, augmente la rétention des participants, améliore les résultats et génère des conclusions plus pertinentes.2,3,4,5 Toutefois, un partenariat efficace exige du temps, des ressources, une bonne communication et une définition claire des rôles.6 Rester CALME est un outil infographique conçu pour guider les conversations difficiles dans les projets de recherche où le partenariat avec les familles est central. Bien que les partenariats commencent souvent avec un plan, la communication peut se détériorer face à des défis inattendus. Rester CALME comprend une infographie et une fiche de travail qui peuvent être utilisées avant ou pendant des conversations difficiles. Cet outil aide les chercheurs et les partenaires familiaux déjà engagés à faire une pause, recentrer leur collaboration et planifier les prochaines étapes pour que l’équipe reste CALME.

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)0.016
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.010
Scholarly communication0.0100.013
Open science0.0020.013
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0130.003

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 head0.115
GPT teacher head0.395
Teacher spread0.280 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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