Patients pris en charge par les Équipes Mobiles Ressources de Gironde à destination de l’aide sociale à l’enfance : une étude descriptive à la recherche d’invariants
Bibliographic record
Abstract
Context: mobile teams have emerged in the last t years to meet the needs of certain young people entrusted to child welfare with complex needs. Our work aims to study this population. Method: we retrospectively analyzed different datas by going through all the files of these minors supported by the mobile teams, from October 1, 2020 to December 31, 2023. Results: while nearly 46% of pregnancies are unwanted, 41% of fathers have not recognized their child and more than half of mothers have a history of psychiatric illness and abuse, only a quarter of these families benefits from perinatal prevention measures. At least 91% of our population has suffered serious abuse. Once the danger has been identified, it takes an average of 2 years and 10 months before the first placement measure, which in 29% of cases is a home placement measure. Young people experiences more than 4 different placement locations. They all have psychiatric symptoms, the first ones appearing at an average age of 4 years. Access to care is delayed by more than two years and 67% of them have been hospitalized. Conduct disorders are omnipresent (nearly 50%), influence the wide prescription of neuroleptics but tend to mask a more broader clinical profile that further complicates the situation. Conclusion: children with complex needs are the result of a succession of failures in terms of prevention, protection and child psychiatric care. Their management requires a multidisciplinary and intersectoral network.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.000 |
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.
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".