Grossesse et travail : Quels sont les risques pour l'enfant à naître ?
Bibliographic record
Abstract
Chaque annee, pres de 530 000 enfants naissent de meres ayant eu une activite professionnelle durant leur grossesse et la majorite d’entre eux sont en bonne sante. Pourtant, malgre toutes les mesures prises, un certain nombre de grossesses presente des complications dont certaines atteignent l’enfant : avortement, mort foetale, naissance prematuree, retard de croissance intra-uterin, malformations congenitales, retard de developpement psychomoteur. La part tenue par les expositions professionnelles sur ces issues fait regulierement l’objet d’interrogations.Ce livre offre un etat des connaissances de l’impact des expositions professionnelles sur le deroulement de la grossesse, particulierement les effets engendres sur l’enfant a naitre. L’ouvrage aborde de nombreux risques : chimiques, biologiques, rayonnements ionisants, ondes electromagnetiques, travail physique, bruit, stress, horaires irreguliers ou de nuit. La reglementation est detaillee ainsi que les resultats des etudes epidemiologiques consacrees a diverses professions. L’experience originale du Quebec sur ce theme est rapportee. Des propositions sont emises afin d’ameliorer la prise en charge de ces risques en milieu professionnel.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".