Hastened death due to disease burden and distress that has not received timely, quality palliative care is a medical error
Bibliographic record
Abstract
All healthcare services strive to achieve the six factors of quality health care - safe, effective, patient-centered, timely, efficient and equitable. Yet multiple structural, process, policy and people factors can combine to result in medical error and patient harm. Measuring the quality of palliative care has many challenges due to its presence across multiple health sectors, variable skill and experience of providers and lack of defined processes for providing services. In Canada there is screening for symptoms and distress in most cancer centers, but not in non-cancer diseases. Screening for distress and disease burden can identify suffering, that when properly addressed, improves quality of life and reduces depression and hopelessness that can lead to requests for hastened death. Our hypothesis is that some requests for hastened death (known as Medical Assistance in Dying or MAiD in Canada) are driven by lack of access to palliative care or lack of quality in the palliative care attempting to address disease burden and distress such that the resulting provision of hastened death is a medical error. The root cause of the error is in the lack of quality palliative care in the previous weeks, months and years of the disease trajectory - a known therapy that the system fails to provide. The evidence for palliative care addressing symptoms and improving quality of life and mood as well as providing caregiver support is established. Early evidence supporting the use of psychotherapeutics in emotional and existential distress is also considered. We present three cases of request for assisted death that could be considered medical error. The paper references preliminary evidence from a review of previous access to palliative care in a limited number of MAiD cases showing that only a minority were identified as having palliative care needs prior to the admission where MAiD was provided. The evidence linking disease burden to hopelessness, depression and hastened death is provided. The many studies revealing the inequity or underservicing of the Canadian population with regards to palliative care are reviewed. We examine a recent framework for palliative care in Canada and point out the need for more aggressive use of standards, process and policies to ensure that Canadians are receiving quality palliative care and that it is equitably accessible to all.
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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 | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".