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Record W2473804989

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

2016· article· en· W2473804989 on OpenAlexaffabout
Sylvie Jetté, Andréanne Tanguay, Lise R. Talbot, Stéphany Westover, Roxana Pavel, Elaine Perreault, Geneviève Mercier

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineCentre Hospitalier de l’Université de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsFrenchMedicineLibrary scienceComputer scienceGeographyArchaeology
DOInot available

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)0.094
metaresearch head score (Gemma)0.239
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.094
Threshold uncertainty score0.530

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0940.239
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0090.004
Scholarly communication0.0080.003
Open science0.0030.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.023
GPT teacher head0.242
Teacher spread0.219 · 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 designBench or experimental
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 routes2
Has abstractyes

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Same venuePubMedSame topicNursing Diagnosis and DocumentationFrench-language works237,207