Does deinstitutionalisation work? Relationships between psychiatric outpatient and inpatient care provision in a rural German catchment area
Bibliographic record
Abstract
Objective: We intended to find out whether an intensification of outpatient care could have an influence on hospitalisations, readmissions, length of stay, coercive measures and involuntary admissions. Method: We investigated the development of some care variables within a district psychiatric hospital responsible for a rural catchment area of 320,000 inhabitants. Associations between inpatient care variables and outpatient activity were assessed by means of multivariate Prais-Winsten regression models for time series. Results: There was a dramatic reduction of mean LOS figures, associated with the activity level of the outpatient clinic. After the conclusion of the deinstitutionalisation process, total number of admissions, cumulative LOS, quotas of involuntary admissions and number of coercive measures did not increase when the number of beds and mean length of stay decreased. Readmissions decreased significantly when outpatient activity increased. Conclusions: Community-oriented ambulatory care on the basis of multi-disciplinary outreach teams seems to be able to reduce high-frequent readmissions and control mean LOS while at the same time a number of coercive measures keep the number of admissions stable. Economic and clinical effects on real inpatient care, however, cannot be definitively evaluated as long as bed provision does not decrease proportionally with the increase of ambulatory activity.
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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 | 0.006 |
| 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 | 0.000 |
| 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".