Accuracy and completeness of Mental Health Act forms applied to involuntary patients admitted to an inpatient psychiatric ward
Bibliographic record
Abstract
Stanislav Pasyk,1 Jennifer Pikard,2 Dane Mauer-Vakil,2 Tariq Munshi2 1School of Medicine, Queen’s University, Kingston, ON, Canada; 2Department of Psychiatry, Kingston Health Sciences Centre, Queen’s University, Kingston, ON, Canada Background: The accuracy and completeness of Mental Health Act forms applied to involuntary patients in an inpatient unit is of paramount importance not only for legal but also for patient safety reasons within a hospital. Materials and methods: This was a retrospective study of 250 patient charts from January 1, 2014 to March 31, 2014. Results: Chart review provided a total of 224 Form 3, 4, 30, and 33 certificates with an overall error rate of 13.19% completion. Of those physicians who completed these certificates, the error rate was 11.63% if a resident physician were to complete and 19.23% if a staff physician were to apply the form. Conclusion: As physicians, there is a legal and moral responsibility to ensure the accuracy of such documentation both ethically and practically as well as a responsibility to the patient and their rights under the Mental Health Act. Keywords: Mental Health Act, Ontario, involuntary, inpatient
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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 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".