A framework for resolving disagreement during end of life care in the critical care unit
Bibliographic record
Abstract
BACKGROUND: End-of-life decisions regarding the administration, withdrawal or withholding of life-sustaining therapy in the critical care setting can be challenging. Disagreements between health care providers and family members occur, especially when families believe strongly in preserving life, and physicians are resistant to providing medically "futile" care. Such disagreements can cause tension and moral distress among families and clinicians. PURPOSE: To outline the roles and responsibilities of physicians, substitute decision makers, and the judicial system when decisions must be made on behalf of incapable persons, and to provide a framework for conflict resolution during end-of-life decision-making for physicians practicing in Canada. SOURCE: We used a case-based example to illustrate our objectives. We employed a comprehensive approach to understanding end-of-life decision making that included: 1) a search for relevant literature; 2) a review of provincial college policies; 3) a review of provincial legislation on consent; 4) a consultation with two bioethicists and 5) a consultation with two legal experts in health law. PRINCIPAL FINDINGS: In Canada, laws about substitute decision-making for health care are primarily provincial or territorial. Thus, laws and policies from professional regulatory bodies on end-of-life care vary across the country. We tabulated the provincial college policies on end-of-life care and the provincial legislation on consent and advance directives, and constructed a 10-step approach to conflict resolution. CONCLUSION: Knowledge of underlying ethical principles, understanding of professional duties, and adoption of a process for mediation and conflict resolution are essential to ensuring that physicians and institutions act responsibly in maintaining a patients' best interests in the context of family-centred care.
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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 | 0.082 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.028 | 0.080 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".