MétaCan
Menu
← Back to cohort

The Experience of the ICNP Francophone Research and Development Centre of Canada with Validating the ICNP® French-Canadian Version

2016· book-chapter· en· W4407854751 on OpenAlexaboutno aff
Jett eacute Sylvie, Tanguay Andr eacute anne, Talbot Lise R., Westover St eacute phany, Pavel Roxana, Perreault Eacute laine, Mercier Genevi egrave ve

Bibliographic record

VenueStudies in health technology and informatics · 2016
Typebook-chapter
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFrenchMedicineGeographyArchaeology

Abstract

fetched live from OpenAlex

The mission of the ICNP Francophone Research and Development Centre of Canada is to promote and support the use of the French ICNP® by French-speaking nursing students, health-care workers and health organizations. This paper presents the different steps performed to achieve validation of the ICNP's French-Canadian version, which is now available for its integration into the electronic health-care records.

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)0.043
metaresearch head score (Gemma)0.050
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: none
Teacher disagreement score0.114
Threshold uncertainty score0.827

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0110.008
Scholarly communication0.0140.004
Open science0.0030.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.056
GPT teacher head0.334
Teacher spread0.278 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueStudies in health technology and informatics→Same topicEmergency and Acute Care Studies→French-language works237,207→