End-of-Life Treatment Preferences of Persons With Serious Mental Illness
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to ascertain preferences for end-of-life care among persons with serious mental illness. METHODS: The participants were 150 community-residing adults with serious mental illness. The Health Care Preferences Questionnaire was administered to obtain information about treatment preferences in response to hypothetical medical illness scenarios: use of pain medication in the case of incurable cancer and use of artificial life support in the case of irreversible coma. Participants were asked what their treatment preferences would be for an imaginary person in each scenario ("other") as well as their preferences for themselves ("self"). RESULTS: For the scenario involving pain medication for incurable cancer, most participants chose aggressive pain management even if cognition might be affected (64 percent of respondents under the "other" scenario and 66 percent under the "self" scenario). Few participants thought a doctor should provide patients with enough medication to end their life (34 percent for self and 24 percent for other). For the scenario involving irreversible coma, respondents were divided in their choice regarding life support. Approximately one-quarter said that they would prefer to immediately terminate life support (28 percent of respondents for other and 29 percent for self), and half said they would choose to turn it off after a defined period (48 percent for other and 45 percent for self). CONCLUSIONS: Persons with serious mental illness were able to designate treatment preferences in response to end-of-life health state scenarios. Future research is needed to test advance care planning methods, assess stability of choices over time, and ascertain the utility of scenario-based preferences to guide end-of-life care decisions in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".