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Record W1983535588 · doi:10.3917/th.644.0321

Profil statistique des affections vertébrales avec indemnités dans l'industrie de la construction au Québec

2001· article· fr· W1983535588 on OpenAlexaffabout
Patrice Duguay, Esther Cloutier, M. Lévy, Pier-Luc Massicotte

Bibliographic record

VenueLe travail humain · 2001
Typearticle
Languagefr
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsGynecologyMedicine

Abstract

fetched live from OpenAlex

RÉSUMÉ Au Québec, en 1995, 1 400 des 6 400 lésions professionnelles survenues dans l’industrie de la construction sont des affections vertébrales. Les manœuvres constituent la profession dont le niveau d’incidence des affections vertébrales est le plus élevé ; suivis par la catégorie des “ autres métiers et occupations ” (ferrailleur, soudeur, homme de service, etc.) ainsi que par les ferblantiers. Sept scénarios d’accidents sont ressortis des analyses multivariées. Les variables les plus statistiquement significatives pour différencier les scénarios sont, par ordre d’importance, le geste exécuté, le genre d’accident, l’agent causal de la blessure, la tâche effectuée et la profession. Les affections vertébrales sont plus souvent qu’attendu associées à l’exécution de tâches connexes aux tâches qualifiées (manutention, tâche préparatoire ou subséquente à une tâche spécialisée, déplacement). Ces résultats font ressortir l’importance d’orienter la prévention et la recherche sur ce type de tâches effectuées par des manœuvres mais aussi d’autres professions (charpentier-menuisier, travailleur de la finition intérieure, etc.).

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.414
Teacher spread0.358 · 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

Citations3
Published2001
Admission routes2
Has abstractyes

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