Physician Perspectives on Legal Processes for Resolving End-of-Life Disputes
Bibliographic record
Abstract
In order to understand how to effectively approach end-of-life disputes, this study surveyed physicians' attitudes towards one process for resolving end-of-life disputes, namely, the Consent and Capacity Board of Ontario. In this case, the process involved examining interpretation of best interests between substitute decision-makers and medical teams. Physicians who made "Form G" applications to the Consent and Capacity Board of Ontario that resulted in a decision posted on the open-access database, Canadian Legal Information Institute (CanLii), were identified and surveyed. This purposive sample led to 13 invitations to participate and 12 interviews (92% response rate). Interviews were conducted using a prescribed interview guide. No barriers to the Consent and Capacity Board process were reported. Applications were made when physicians reached an impasse with the family and further treatment was perceived to be "unethical." The most significant challenge reported was the delay when appeals were launched. Appeals extended the process for an indefinite period of time making it so lengthy it negated any perceived benefits of the process. Benefits included that a neutral third party, namely the Consent and Capacity Board, was able to assess best interests. Also, when decisions were timely, further harm to the patient was minimized. Physicians reported this particular approach, namely the Consent and Capacity Board has a mechanism that is worthwhile, patient centred, process oriented, orderly and efficient for resolving end-of-life disputes and, in particular, determining best interests. However, unless the appeal process can be adjusted to respond to the ICU context there is a risk of not serving the best interest of patients. Physicians would recommend framing end-of-life treatment plans in the positive instead of negative, for example, propose palliative care and no escalation of treatment as opposed to withdrawal.
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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 | 0.036 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| 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".