Evaluating the closure or downsizing of psychiatric hospitals: social or clinical event?
Bibliographic record
Abstract
OBJECTIVES: The evaluation matrix recently proposed by Tansella and Thornicroft suggests that the field of social and epidemiological psychiatry has focussed more on the individual/patient level of mental health care services than the system level. Moreover, phenomena such as deinstitutionalization have been examined more as clinical events than as social ones. The aims here are to deepen our understanding of deinstitutionalization, particularly as regards the downsizing/closure and role of psychiatric hospitals. METHODS: I begin by reviewing the manifest and latent functions of psychiatric hospitals. This is followed by a discussion of how these functions must be met by any comprehensive community-oriented system of mental health care for severely mentally ill patients. Also, in order to reframe the downsizing/closure of psychiatric hospitals as a social event for the field of social psychiatry and psychiatric epidemiology, I posit that the process of deinstitutionalization is driven today by the same forces that were present at the outset of the movement. RESULTS: I review four recent series of studies addressing primarily the outcomes, but also other aspects, of the downsizing/closure of psychiatric hospitals, with a view to illustrating the methods used, the results obtained and the blind angles missed in this research. CONCLUSIONS: Lessons are drawn on how to fill certain vacant cells of the matrix.
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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 | 0.059 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".