Compulsory Hospitalization, Severity of Disorders and Territorial Landscape: A French Study
Bibliographic record
Abstract
INTRODUCTION: The objective of this study, carried out in France, was to analyse important psychiatric sector disparities in the rate of compulsory hospitalizations as a function of the severity of disorders among the people hospitalized, and of certain variables linked to the territorial landscape (socio-demographic context, and primary and psychiatric care offer).METHODS: The 125 sectors that took part in this study were divided into three groups on the basis of their compulsory hospitalization rates.RESULTS: The results did not reveal any link between compulsory hospitalization rate and severity of disorders.The hospitalization rate was correlated with variables specific to urban areas: it was higher in more densely populated areas with a larger proportion of people living alone and a greater number of shelters and social rehabilitation centres. It was also higher in the sectors with larger hospitalization capacity, with longer mean hospitalization durations, but with a lower rate of resort to psychiatry and larger human resources.CONCLUSIONS: The frequency of resort to involuntary hospitalization in France does not seem to be linked to the severity of patients’ disorders, but it is higher in sectors with a profile specific to urban areas, larger hospitalization capacities and human resources.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".