MétaCan
Menu
Back to cohort
Record W7025095313

Troubles musculosquelettiques chez les ferrailleurs : facteurs de risque et pistes de prévention

2022· article· fr· W7025095313 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsRisk factorSet (abstract data type)Term (time)Identification (biology)
DOInot available

Abstract

fetched live from OpenAlex

AperuLes ferrailleurs sont des travailleurs du secteur de la construction qui posent et assemblent l'acier d'armature pour renforcer le bton.Ces derniers sont exposs plusieurs risques (postures et mouvements contraignants, manutention de charges lourdes, etc.) qui les prdisposent dvelopper des troubles musculosquelettiques (TMS).Il en rsulte que ce mtier a la rputation d'tre difficile physiquement, ce qui entrane des problmes de recrutement et de rtention des travailleurs. ObjectifDocumenter, partir de la littrature, les principaux facteurs de risque de TMS auxquels sont exposs les ferrailleurs, ainsi que les pistes de prvention qui leur sont associes. Faits saillants La pose et l'assemblage d'acier d'armature pourraient exposer les ferrailleurs des facteurs de risque de TMS, tels que :le maintien d'une flexion prononce du tronc, l'excution de mouvements rapides et rptitifs du poignet, la manutention de charges lourdes, le travail sur des surfaces ingales ou instables, l'intensit excessive du travail physique. Des outils motoriss ont t conus dans le but de rduire les postures et mouvements contraignants qui sont associs la ligature manuelle de l'acier d'armature avec une pince.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0220.000

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 head0.073
GPT teacher head0.425
Teacher spread0.353 · 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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicOccupational Health and PerformanceFrench-language works237,207