Medical Advance Care Planning for Persons With Serious Mental Illness
Bibliographic record
Abstract
OBJECTIVE: This study examined preferences regarding medical advance care planning among persons with serious mental illness, specifically, experience, beliefs, values, and concerns about health care proxies and end-of-life issues. METHODS: A structured interview, the Health Care Preferences Questionnaire, was administered to a convenience sample of 150 adults with serious mental illness who were receiving community-based services from the Massachusetts Department of Mental Health. Clinical information and demographic data were also collected. RESULTS: A total of 142 participants completed the questionnaire. Although more than one-quarter had thought about their medical treatment preferences in the event that they became seriously medically ill, very few had discussed these preferences. A majority of respondents (72 percent) believed that someone should be designated to make medical health care decisions for a person who is too sick to make or communicate these decisions him- or herself. Common end-of-life concerns included financial and emotional burdens on family, pain and suffering, interpersonal issues such as saying "goodbye," spiritual issues, and funeral arrangements. Participants were most uneasy about the prolonging or stopping of life support by proxy decision makers. A total of 104 respondents (69 percent) expressed interest in formally selecting a health care proxy. CONCLUSIONS: Although persons with serious and persistent mental illness have little experience with medical advance care planning, they show substantial interest in it. Furthermore, they are able to consider and communicate their preferences. This study supports the feasibility, acceptability, and utility of a standardized approach to medical advance care planning with this population.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".