MétaCan
Menu
Back to cohort
Record W3092107775 · doi:10.7202/1071692ar

De l’analyse du travail sensoriel lors de prises de décision clinique au co-design d’un scénario de simulation

2020· article· fr· W3092107775 on OpenAlexaffabout
Sylvie Grosjean, Luc Bonneville

Bibliographic record

VenuePhronesis · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesSociologyPhilosophy

Abstract

fetched live from OpenAlex

La dimension sensorielle de la prise de décision clinique pose tout un défi pour la formation des professionnels de la santé. Par conséquent, comment stimuler chez eux une forme d’acuité sensorielle ? Nous avons mené une recherche en partenariat avec le laboratoire de simulation d’un hôpital canadien afin de comprendre le travail sensoriel effectué par des infirmières lors d’une prise de décision clinique. Pour ce faire, nous avons analysé les interactions se déroulant lors des groupes de discussion afin de révéler le travail sensoriel accompli par les infirmières lors de l’évaluation clinique de patients fictifs (personas).

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)0.066
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 score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.066
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.544
GPT teacher head0.602
Teacher spread0.057 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePhronesisSame topicHealth Policy Implementation ScienceFrench-language works237,207