Premières identifications d’un profil traumatique chez des patients hospitalisés en psychiatrie en Martinique
Bibliographic record
Abstract
The population hospitalised in psychiatry seems more exposed to traumatic events than the French general population, with particularly more sexual aggressions. The aim of this study is to describe the population hospitalised in psychiatry and more precisely the traumatic history of these patients, their comorbidities (mental diseases and addictions), and socio economical level. This descriptive, cross sectional and retrospective study took place in the Crisis Center in the University Hospital in Martinique (French West Indies), from February to July 2013. A socio-demographic information, the Mini International Neuropsychiatric Interview 5.0, the Trauma History Questionnaire and the Impact Events Scale-Revised were realised with 49 of the 143 patients admitted during this period (34.3%). In this population, we found a mean of 6.5 (standart-deviation=4.2) different types of traumatic event, with 38.8% patients reporting a natural disaster, and 38.8% declaring at least one sexual aggression. In the 25 patients suffering from post-traumatic stress disorder, 66.7% underwent a sexual aggression, significatively during childhood (before 10 years old, P=0.01), and during adolescence (between 10 to 18 years old, P=0.01). These results underline the importance of a systematic screening of the traumatic profile: the characteristics of the traumatic events and its clinical impact.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".