Forty years without mental hospitals in Italy
Bibliographic record
Abstract
In 1978 Italy implemented Law Number 180, the reform law that blocked all new admissions to public mental hospitals. After 40 years without mental hospitals, we aim at understanding the consequences of the Italian reform in terms of mental health care facility and staff availability. We compared the organization of the Italian mental health system with that of countries belonging to the Group of 7 (G7) major advanced economies. Italy has nearly 8 psychiatrists, 20 nurses, 2 social workers and less than 3 psychologists per 100,000 population, while for example in France there were 22 psychiatrists, in Japan 102 nurses, in the United States 18 social workers, and in Canada and France more than 45 psychologists per 100,000 population. In terms of inpatient facilities, no beds in mental hospitals were available in Italy, while in the other G7 countries mental hospital beds ranged from 8 in the United Kingdom to 204 in Japan per 100 000 population. In Italy there were fewer beds for acute care in general hospitals but more beds in community residential facilities than in the other G7 countries. Service use data showed variability in the provision of mental health care throughout the country. Soon after the implementation of the Italian reform the absolute number of compulsory admissions progressively declined, from more than 20,000 in 1978 to less than 9000 in 2015. Alongside the progressive decline of psychiatric beds imposed by Law 180, the age-adjusted suicide rate remained stable, ranging from 7·1/100,000 population in 1978 to 6·3/100,000 population in 2012. The population of psychiatric patients placed in Italian forensic psychiatric hospitals progressively declined. During the last 40 years without mental hospitals, Italy has seen a progressive consolidation of a community-based system of mental health care. We highlighted, however, reasons for concern, including a decreasing staffing level, a potential use of community residential facilities as long-stay residential services, a still too high variability in service provision across the country, and lack of national data on physical restraints. At a national level, the resources allocated to mental health care are lower in Italy than in other high-income countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".