Nonbeneficial Treatment Canada
Bibliographic record
Abstract
OBJECTIVE: Many healthcare workers are concerned about the provision of nonbeneficial treatment in the acute care setting. We sought to explore the perceptions of acute care practitioners to determine whether they perceived nonbeneficial treatment to be a problem, to generate an acceptable definition of nonbeneficial treatment, to learn about their perceptions of the impact and causes of nonbeneficial treatment, and the ways that they feel could reduce or resolve nonbeneficial treatment. DESIGN: National, bilingual, cross-sectional survey of a convenience sample of nursing and medical staff who provide direct patient care in acute medical wards or ICUs in Canada. MAIN RESULTS: We received 688 responses (response rate 61%) from 11 sites. Seventy-four percent of respondents were nurses. Eighty-two percent of respondents believe that our current means of resolving nonbeneficial treatment are inadequate. The most acceptable definitions of nonbeneficial treatment were "advanced curative/life-prolonging treatments that would almost certainly result in a quality of life that the patient has previously stated that he/she would not want" (88% agreement) and "advanced curative/life-prolonging treatments that are not consistent with the goals of care (as indicated by the patient)" (83% agreement). Respondents most commonly believed that nonbeneficial treatment was caused by substitute decision makers who do not understand the limitations of treatment, or who cannot accept a poor prognosis (90% agreement for each cause), and 52% believed that nonbeneficial treatment was "often" or "always" continued until the patient died or was discharged from hospital. Respondents believed that nonbeneficial treatment was a common problem with a negative impact on all stakeholders (> 80%) and perceived that improved advance care planning and communication training would be the most effective (92% and 88%, respectively) and morally acceptable (95% and 92%, respectively) means to resolve the problem of nonbeneficial treatment. CONCLUSIONS: Canadian nurses and physicians perceive that our current means of resolving nonbeneficial treatment are inadequate, and that we need to adopt new techniques of resolving nonbeneficial treatment. The most promising strategies to reduce nonbeneficial treatment are felt to be improved advance care planning and communication training for healthcare professionals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.181 | 0.014 |
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".