Effects of a statutory reform on waiting times for outpatient psychotherapy: A multicentre cohort study
Bibliographic record
Abstract
Abstract Aims Social inequality in access to mental health care is a current concern across the world. The authors determined whether differences in waiting times for outpatient psychotherapy changed after a statutory reform of the German psychotherapy law. Methods The dates of first contact, first visit and treatment start, along with socio‐demographic and clinical data, were extracted from patient records in community‐based psychotherapy practices. Predictors of waiting times for first visit and treatment start were investigated using multilevel Cox regression models to estimate adjusted hazard ratios (HRadj). Results Data from 1548 patient records from nine practices were extracted. Before the reform, the time span between first contact and first visit was longer for patients with compulsory education than for patients with a college degree (HRadj 0.8, 95% CI 0.6–1.0), whereas this was no longer the case after the law changed (HRadj 1.0, 95% CI 0.8–1.3). Patients whose treatment was covered by the state were at higher risk of a long waiting time from last visit to treatment start compared with patients with statutory health insurance after the law changed (HRadj 0.4, 95% CI 0.3–0.7), which had not been the case before the law changed (HRadj 1.3, 95% CI 0.8–2.2). Conclusions Social inequality in access to psychotherapy was reduced in part by the updated psychotherapy law in terms of educational groups; however, it increased in other aspects. This shows how political decisions can powerfully impact clinical practice, ultimately helping one group of patients while disadvantaging another.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".