Améliorer l’évaluation et l’accompagnement à la reprise et au maintien en emploi de personnes souffrant d’un trouble psy-chiatrique et/ou neurologique : Étude préliminaire
Bibliographic record
Abstract
L’objectif de cette étude vise à repérer rapidement des déterminants mentaux de difficulté au maintien ou à l’insertion en emploi de personnes souffrant d’une maladie psychiatrique et/ou neurologique inscrites dans un programme d’insertion au travail. Soixante et une personnes sui-vies dans le cadre d’un tel programme au sein de l’EPNAK de Soisy-sur-Seine ont été incluses dans cette étude. L’activité cérébrale (EEG) a été mesurée pour les biomarqueurs de la douleur chronique, de l’insomnie chronique, du surmenage, du syndrome d’anxiété sociale et le syndrome dépressif. Les résultats préliminaires ont montré que le facteur dominant était l’insomnie chro-nique, suivi par le facteur de surmenage. L’analyse de corrélation entre les facteurs met en évi-dence un lien significatif entre la dépression et l’anxiété. En conclusion, le repérage précoce de ces facteurs permettra un accompagnement adapté et sur mesure des personnes en situation de han-dicap vers l’insertion professionnelle. The purpose of this study is to quickly identify mental determinants of difficulty in maintaining or entering employment for individuals suffering from psychiatric and/or neurological illnesses enrolled in a work integration program. Sixty-one individuals participating in such a program at EPNAK in Soisy-sur-Seine (France) were included in this study. Brain activity (EEG) was meas-ured for biomarkers of chronic pain, chronic insomnia, burnout, social anxiety syndrome, and depressive syndrome. Preliminary results showed that the dominant factor was chronic insom-nia, followed by burnout. Correlation analysis between the factors highlighted a significant link between depression and anxiety. In conclusion, early identification of these factors will allow for tailored and personalized support for individuals with disabilities towards professional inte-gration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".