Everyday ethics of suicide care: Survey of mental health care providers’ perspectives and support needs
Bibliographic record
Abstract
Suicide occurs in people of all ages and backgrounds, which negatively affects families, communities, and the health care providers (HCPs) who care for them. The objective of this study was to better understand HCPs' perspectives of everyday ethical issues related to caring for suicidal patients, and their perceived needs for training and/or support to address these issues. We conducted a mixed methods survey among HCPs working in mental health in Québec, Canada. Survey questions addressed their perspectives and experiences of everyday ethical challenges they encounter in their practice with people who are suicidal, and their perceived needs for training and/or support therein. 477 HCPs completed the survey. Most participants mentioned encountering ethical issues when caring for people who are suicidal. The challenges HCPs encounter in their practice with people who are suicidal are numerous, including issues related to maintaining privacy, confidentiality, freedom and the therapeutic relationship. The lack of time, resources and professional support to address these issues was emphasized. Most HCPs reported that the training or education they have received does not allow them to address everyday ethical issues related to suicide care. In sum, there is a clear reported need for better training and support for HCPs who are offering care to people who are suicidal in relation to everyday ethical issues they encounter. Implications for practice include providing greater access to training, including access to specialists in ethics to address specific issues. This additional support could alleviate morally distressing situations for HCPs.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".