Méthodologie d’analyse et de surveillance pour la prévention des arrêts maladie
Bibliographic record
Abstract
At a time when sick leave is a sign of growing ill-being for workers and a cost burden for the society, the systematic digitalization and distribution of data offers great opportunities for its prevention. We have therefore taken advantage of this opportunity to develop a range of prevention tools based on statistical analysis methods. In a first part, this work proposes an analysis of the mechanisms explaining sick leave among workers. The analysis of a national survey has first identified and prioritised their main determinants using random forest. Then, an analysis of administrative data had helped to identify absence trajectories that could lead to serious sick leaves thanks to sequential analyses and multi-state modelling. In a second step, tools were developed to identify abnormal situations of sick leave at company level. A company typology was first built to produce benchmark values for companies to accurately assess their situation. Finally, an algorithm for identifying absence peaks, adapted from epidemiological surveillance models, was finally developed to automatically identify companies in difficulty.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".