Involuntary commitment and medical education on mental health diseases across Europe
Bibliographic record
Abstract
Abstract Background Psychiatric diseases are projected to become one of the greatest contributors to the global burden of disease by 2030, already presenting as one of the principal causes of DALYs lost in Europe. Given the particular nature of psychiatric disorders, national legislatures have been enacted by each European country regarding the possibility of involuntary psychiatric treatment. As practical implementations vary greatly from country to country, we wish to analyse how different attitudes to involuntary treatment affect health outcomes, so as to propose a uniform guideline for European medical practitioners. We also wish to analyse whether medical education targeted at communication with psychiatric patients has an effect on involuntary treatment rates and general mental well-being. Methods We conducted a systematic review on PubMed to identify studies pertaining to how legislature on involuntary commitment varies between European countries, as well as to what extent it is utilised. We also looked at the extent of medical education on psychiatric diseases. Results Preliminary results show that involuntary hospitalisation rates vary greatly within Europe, with certain countries being almost 20 times more likely to utilise such measures than others, notwithstanding similar mental illness prevalence. Results do not seem correlated to legislation types. Conclusions Given the vastly different use of involuntary commitment, a more standardised European approach should be implemented, especially in sight of the growing prevalence and burden of disease of psychiatric illnesses. Furthermore, an often-overlooked aspect of medical education is how to understand and communicate effectively with patients dealing with mental diseases: we advocate for continuous education, regardless of medical specialty. Key messages • Involuntary hospitalisation rates vary greatly across Europe, but the effects of this phenomenon have been poorly studied. We propose a more unified approach to maximise the efficacy of such a law. • We advocate for a more thorough education of health professionals on mental illnesses, regardless of medical specialty, in sight of the growing prevalence of such diseases.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".