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Record W6888875557 · doi:10.25318/1310068001-fra

Endroits le plus souvent contactés pour de l'information ou des conseils en matière de santé, selon le moment de la journée, population à domicile de 15 ans et plus, Canada

2019· dataset· fr· W6888875557 on OpenAlexaboutno aff

Bibliographic record

VenueStatistics Canada Dissemination · 2019
Typedataset
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationPersonal autonomyMedical journal

Abstract

fetched live from OpenAlex

Ce tableau contient 288 séries, avec des données pour les années 2001 - 2001 (il n'y a pas nécessairement de données pour toutes les années pour l'ensemble des combinaisons). Ce tableau contient des données telles que décrites par les dimensions suivantes (les combinaisons ne sont pas toutes disponibles) : Géographie (1 éléments : Canada ...) Endroit le plus souvent contacté pour de l'information ou des conseils en matière de santé (12 éléments : Information ou conseils en matière de santé; cabinet du médecin; Information ou conseils en matière de santé; clinique sans rendez-vous; Information ou conseils en matière de santé; clinique communautaire; Information ou conseils en matière de santé; ligne d'information téléphonique ...) Moment de la journée, endroit le plus souvent contacté pour de l'information ou des conseils en matière de santé (3 éléments : Information ou conseils en matière de santé durant les heures normales de bureau; Information ou conseils en matière de santé en soirée et durant les fins de semaine; Information ou conseils en matière de santé pendant la nuit ...) Caractéristiques (8 éléments : Limite inférieure de l'intervalle de confiance de 95 %; nombre de personnes; Nombre de personnes; Limite supérieure de l'intervalle de confiance de 95 %; nombre de personnes; Coefficient de variation; nombre de personnes ...).

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.012
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.008

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 head0.007
GPT teacher head0.276
Teacher spread0.269 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

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Same venueStatistics Canada DisseminationFrench-language works237,207