Community-based mental health care: to what extent are service costs associated with clinical, social and service history variables?
Bibliographic record
Abstract
BACKGROUND: The growing movement in many European countries towards capitation-based systems for financing mental health care has generated increasing interest in developing appropriate models capitation formulae. The aims of the study were: to detect and compare any differences in service costs between patients with different diagnoses; and to analyse the associations between patient characteristics and service costs. METHODS: All patients in contact with the South-Verona Community Mental Health Service during the last quarter of 1996 were included in the study. Clinical and service-related variables were collected at first index contact; 3 months later, patients were interviewed using the Client Services Recipient Interview. For those who completed both the clinical assessments and the services receipt schedule (N = 339), 1-year psychiatric and non-psychiatric direct care costs were calculated. Weighted backward regression analyses were performed. RESULTS: The most significant variables associated with psychiatric costs were: admission to hospital in the previous year; intensity and duration of previous contacts with South-Verona CMHS; being unemployed; having a diagnosis of affective disorder; and, Global Assessment of Functioning score. The final model explained 66% of the variation in costs of psychiatric care and 13 % of variation in non-psychiatric medical costs. CONCLUSIONS: The model presented in this study explains a higher degree of cost variance than previously published studies. In community-based services more resources are targeted towards the most disabled patients. Previous psychiatric history (number of admissions in the previous year and intensity of psychiatric contacts lifetime) is strongly associated with psychiatric costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".