Are People Worse Off in a Mental Health Treatment Paradigm Where Medication Is Deemphasised? A Naturalistic Noninferiority Trial of an Initiative to Improve Patient Choice
Bibliographic record
Abstract
Background: Norwegian health authorities have established dedicated units for medication-free mental health treatment (MFT) to enhance patient choice. These services place greater emphasis on psychosocial and psychotherapeutic interventions than traditional care. They aim to be free from coercion and pressure regarding medication, rather than totally absent of medication. Aims: This study evaluates whether outcomes for patients receiving MFT are noninferior to those receiving treatment as usual (TAU). Method: A noninferiority analysis was conducted using the Outcome Questionnaire-45.2 (OQ-45.2) to assess changes from admission to discharge. Two datasets were analysed: a smaller research sample with repeated measures and two comparison units (Sample R; n = 59 + 124), and a larger quality register sample with single-measure data and one comparison unit (Sample Q; n = 140 + 238). In sample R, associations between clinical and demographic variables and treatment outcomes were also explored. Results: Participants in both treatment conditions showed substantial improvement. In Sample R, changes between groups were not statistically significant, and the noninferiority analysis was inconclusive. In Sample Q, intention-to-treat analyses indicated superiority for MFT, while sensitivity analyses excluding dropouts supported noninferiority. Conclusions: Findings suggest that MFT is not associated with inferior short-term treatment outcomes in the population currently receiving this care. These results may reassure clinicians and policymakers in supporting patient choice and may assist patients in selecting their preferred treatment approach. This study is registered at ClinicalTrials.gov (NCT03499080), date 17/04/2018.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".