What do people write about after filling out a survey on extending assistance in dying to incompetent patients with dementia?
Bibliographic record
Abstract
Abstract Background In December 2015, the Canadian province of Quebec legalized medical aid in dying (MAID) for competent patients who satisfy strictly defined criteria. Like the rest of Canada, Quebec is considering extending the legislation to incompetent patients under certain conditions not yet specified. With the objective of informing policy developments in this sensitive area, we recently conducted a province‐wide postal survey to assess stakeholders’ attitudes toward giving incompetent patients with dementia access to MAID in some circumstances. Objective: To describe the comments respondents left at the end of the questionnaire. Method The questionnaire was mailed to 2,125 randomly selected representatives of four stakeholder groups: adults 65 and over presumed free of cognitive impairment, informal caregivers of persons with dementia, and nurses and physicians who were caring for patients with dementia at the time of the survey. A total of 1,050 questionnaires were returned (49.4%), of which 420 (40%) included comments. These ranged from a few words to several pages of free text. Comments were independently coded by the two authors into themes, topics and subtopics. Result Seven major themes were identified, under which a total of 1,148 units were classified. Respondents wrote (1) about themselves (297 units), (2) the survey and questionnaire (204), (3) patients with dementia (235), (4) their informal caregivers (97), (5) the health care system (75), (6) the MAID legislation (207), and (7) society in general (33). Each theme comprises between two and five topics. Topics include, for instance, the respondent’s personal values and beliefs, informal caregivers’ difficulty in making end‐of‐life decisions for a loved one, and health care professionals’ lack of training in caring for patients dying with dementia. Theme 6, on the MAID legislation, includes two topics (the current legislation and its extension), the second of which was subdivided into five subtopics for greater refinement: preferred alternatives to MAID, fears should it be extended to incompetent patients, advanced requests, anticipated difficulties in providing MAID to incompetent patients, and eligibility criteria. Conclusion In their comments, respondents raised several challenges associated with the possible expansion of MAID to incompetent patients that need attention.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".