Why Service Users Choose Medication-Free Psychiatric Treatment: A Mixed-Method Study of User Accounts
Bibliographic record
Abstract
PURPOSE: Medication has been a central part of treatment for severe mental disorders in Western medicine since the 1950s. In 2015, Norwegian Health Authorities decided that Norwegian health regions must have treatment units devoted to medication-free mental health treatment to enhance service users' freedom of choice. The need for these units has been controversial. The aim of this study was to examine why service users choose medication-free services. This article examines what purpose these units serve in terms of the users' reasons for choosing this service, what is important for them to receive during the treatment, and what factors lay behind their concerns in terms of medication-related views and experiences. METHODS: Questionnaires were answered by 46 participants and 5 participants were interviewed in a mixed-method design integrated with a concurrent triangulation strategy applying thematic analysis and descriptive statistics. RESULTS: Negative effects of medications and unavailable alternatives to medication in ordinary health care were important reasons for wanting medication-free treatment. Medication use may conflict with personal values, attitudes, and beliefs. CONCLUSION: This study broadens the understanding of why the demand for separate medication-free units has arisen. The findings may contribute to making medication-free treatment an option in mental health care in general. To this end, clinicians are advised to communicate all treatment alternatives to service users and to be mindful of the effect of power imbalances in their interactions with them.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".