Mental Capacity for Treatment Decisions in Psychiatry Inpatients
Bibliographic record
Abstract
This research aimed to assess the prevalence of mental capacity for treatment decisions in psychiatry inpatients in Ireland and elucidate the relationship, if any, between mental capacity and key clinical and demographic factors. As part of this work, a systematic review was completed, which aimed to examine the literature to determine the extent of the research and existing data in this field. This research aimed to compare assessments of mental capacity based on legal criteria with those based on clinical criteria for mental capacity to establish the concordance, if any, between these two approaches to assessing mental capacity. To examine this, mental capacity for treatment decisions was assessed in 215 psychiatry inpatients (176 voluntary and 39 involuntary) in four psychiatry units using both legal criteria (Ireland¿s Assisted Decision-Making (Capacity) Act 2015) and clinical criteria (the MacArthur Competence Assessment Tool for Treatment; MacCAT-T). On multi-variable linear regression analysis using linear scores of the MacCAT-T, mental capacity was significantly associated with voluntary admission status, being employed, having a primary diagnosis other than schizophrenia or a related disorder, and younger age. Together, these factors accounted for 44.4% of the variance in mental capacity between participants. The MacCAT-T scores were adapted to establish categorical mental capacity. Overall, 1.9% of participants lacked mental capacity for treatment decisions; 50.7% had partial mental capacity; and 47.4% had full mental capacity. The relatively high rate of ¿partial mental capacity¿ identified suggests that decision-making supports which have been implemented with the Assisted Decision- Making (Capacity) Act 2015 are likely to be of substantial importance in assisting psychiatry inpatients making decisions about treatment in the future. According to the legal criteria as assessed using the criteria of The Assisted Decision-Making (Capacity) Act 2015, over one third of participants (34.9%) lacked mental capacity for treatment decisions. Patients who lacked mental capacity according to the legislation scored significantly lower on all subscales of the MacCAT-T than patients who had mental capacity. This leads to the conclusion that there is close correlation between mental capacity assessments based on legal and clinical criteria. These findings support the 2015 Act¿s current legal definitions of mental incapacity.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".