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Record W4377004879 · doi:10.7202/1095890ar

Intervenir sur l’absence au travail pour améliorer la santé des individus et de l’organisation : une approche contingente basée sur des résultats de recherche

2023· article· fr· W4377004879 on OpenAlexaff
Roland Foucher

Bibliographic record

VenueHumain et Organisation · 2023
Typearticle
Languagefr
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Depuis plus de trois quarts de siècle, des milliers de recherches en psychologie du travail et des organisations ont jeté un éclairage sur cinq aspects de l’absentéisme : sa nature, sa mesure, ses effets, ses causes et les moyens de le réguler. Le corpus de connaissances dégagé des textes analysés a inspiré la formulation de cinq prémisses, soit une par aspect. Celles-ci constituent les fondements d’un modèle original d’analyse de l’absentéisme qui encadre la démarche d’intervention proposée dans ce texte. En plus d’être basée sur des résultats de recherche, l’approche sous-tendant ce modèle et cette démarche conçoit l’absentéisme comme un phénomène contingent, donc variable selon les contextes et les individus, et aux causes multiples.

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)0.044
metaresearch head score (Gemma)0.055
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0030.008
Scholarly communication0.0060.007
Open science0.0040.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.213
GPT teacher head0.436
Teacher spread0.222 · 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
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
Published2023
Admission routes1
Has abstractyes

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