Outcomes of involuntary hospital admission. Satisfaction with treatment and the effect of involuntary admissions on patients
Bibliographic record
Abstract
Introduction Involuntary hospitalization in those presumed to be mentally ill has been a common practice. Although some patients are hospitalized for aggression, two-thirds of the patients are hospitalized because of the threat they pose to themselves. Although these patients require risk assessment and evaluation for possible presence of mental illness, the question is how much these patients will benefit from involuntary admission and what the long-term outcome would be. Method All patients admitted involuntary to the psychiatric ward in Kingston, Canada, and psychiatrists involved in their care were interviewed to see whether they think the involuntary admission was helpful. All patients were asked to fill-out MacArthur AES to assess their satisfaction with hospitalization. Results Although psychiatrists frequently reported that the admission was justified, only 29 out of 81 patients reported being explained to why they had been admitted involuntarily. Also, there was a significant difference in AES scores between those who were and were not given an explanation for admission. In addition, psychiatrists more often reported that the involuntary admission worsened the therapeutic relationship which was significantly associated with involuntary admission that was not explained to patients. Discussion The results of our study shows that patients admitted involuntarily often feel disappointed with staff and mental health system. It could lead to feeling of hopelessness, frustration and low self-esteem. If explained, some patients who present with risk to self might accept voluntary admissions, that will improve therapeutic alliance with psychiatrists and increase satisfaction from hospitalization. Result of this study could improve the decision making process for involuntary admissions. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".