Reinstitutionalisation in mental health care: comparison of data on service provision from six European countries
Bibliographic record
Abstract
OBJECTIVE: To establish whether reinstitutionalisation is occurring in mental health care and, if so, with what variations between western European countries. DESIGN: Comparison of data on changes in service provision. SETTING: Six European countries with different traditions of mental health care that have all experienced deinstitutionalisation since the 1970s--England, Germany, Italy, the Netherlands, Spain, and Sweden. OUTCOME MEASURES: Changes in the number of forensic hospital beds, involuntary hospital admissions, places in supported housing, general psychiatric hospital beds, and general prison population between 1990-1 and 2002-3. RESULTS: Forensic beds and places in supported housing have increased in all countries, whereas changes in involuntary hospital admissions have been inconsistent. The number of psychiatric hospital beds has been reduced in five countries, but only in two countries does this reduction outweigh the number of additional places in forensic institutions and supported housing. The general prison population has substantially increased in all countries. CONCLUSIONS: Reinstitutionalisation is taking place in European countries with different traditions of health care, although with significant variation between the six countries studied. The precise reasons for the phenomenon remain unclear. General attitudes to risk containment in a society, as indicated by the size of the prison population, may be more important than changing morbidity and new methods of mental healthcare delivery.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".