Clinical presentations of suicidality in relation to medical assistance in dying
Bibliographic record
Abstract
INTRODUCTION: This study explores the assessments of mental health clinicians working with suicidal patients who requested access to medical assistance in dying (MAiD). METHODS: A sample of convenience completed an online questionnaire about their experiences with suicidal patients. Respondents described their encounters with 227 suicidal patients: 44 requested access to MAiD, and 183 did not. Data were analyzed using chi-squared and simple t-test to identify differences between the respondents' descriptions of the two groups. RESULTS: Results noted differences between patients who experience suicide ideation and request MAiD (SPM), and those who experience suicide ideation and do not (SP). Overall, the SPM group was older, more physical health concerns, chronic pain, existential distress, and less hope. Many had experienced several episodes of mental health care and medication trials, though unlike the SP group, they had a split between accessing a little care and a lot of care. They also engaged in less suicide planning, and some had no history of suicide attempts. CONCLUSION: It is important that mental health clinicians learn to differentiate between MAiD requests due to an ongoing and irremediable mental disorder, and MAiD requests in response to circumscribed psychological suffering that could be relieved via a change in circumstances and/or access to different treatment options amenable to the patient.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".