A naturalistic study of outcomes in a general psychiatry day hospital
Bibliographic record
Abstract
Psychiatric day hospitals have potential advantages compared with inpatient and/or outpatient treatment, but results are inconsistent and thus their use is controversial. Moreover, few data are available on the factors influencing the treatment success; this is particularly the case regarding Swiss day hospitals. This paper has three goals; first, to give an insight into the population attending a new general day hospital in Canton Fribourg; second, to assess different treatment outcome indicators, and third, to assess treatment improvement predictors. Results, relying on therapist-assessed data from all day hospital stays gathered over more than 3 years, show that, within a wide diversity, overall patients had a significant improvement in their functioning level during the stay. Moreover, our results indicated that patients with a substance-related disorder benefited less from the treatment, whereas patients with an affective disorder benefited more. Additionally, patients starting with a lower functioning level improved more, whereas patients with a longer disorder history improved less. Suicidality and self-harming behaviour did not affect the outcome. Most importantly for clinical implications, longer treatment and more frequent attendance at the day hospital predicted a better outcome. Results are discussed in the context of the existing literature and their potential utility for further treatment and research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".