Is the Norwegian stepped care model for allocation of patients with mental health problems working as intended? A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Stepped care models are frameworks for mental health care systems in several countries. According to Norwegian guidelines, individuals with mental health problems of mild severity should be treated in community mental health services, moderate severity in specialist mental health services, while complex/severe problems are often a shared responsibility. This study investigated whether patients are allocated as intended. METHODS: In a cross-sectional study, 4061 outpatients recruited from community- and specialist mental health services reported demographic variables, symptoms of anxiety/depression, functional impairment, health status, and sick leave status. The community sample consisted of two subsamples: mild/moderate problems and complex problems. RESULTS: There was substantial overlap (80%-99%) of symptoms, impairment, and health between community- and specialist mental health services. More impairment, worse health, lower age, and being male were associated with treatment at specialist level compared to community mild/moderate. Better health, being in a relationship, and lower age were associated with specialized treatment compared to community complex group. CONCLUSION: The limited association between treatment level and symptoms and functional impairment reveals inconsistencies between treatment guidelines and clinical practice. How the existing organization affects patient outcomes and satisfaction should be investigated further.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".