Barriers and Ethical Implications of the Ontario Ministry of Health and Long-Term Care Do Not Resuscitate Confirmation Form
Bibliographic record
Abstract
Background: Goals of care (GOC) planning involves healthcare providers (HCPs) discussing patients’ health preferences, including code status options ranging from “full code” (cardiopulmonary resuscitation [CPR] and intubation) to “do not resuscitate (DNR)”. In 2008, Ontario introduced the Do Not Resuscitate Confirmation Form (DNR-CF), which permits first responders to withhold CPR when a valid form is present. Despite routine GOC conversations during hospital admissions, few physicians complete DNR-CFs to guide community-based emergency responders. Objective: We aimed to identify the completion rates and perceived barriers to completing DNR-CFs among general internists. Methods: We conducted an online survey of general internists at two Hamilton hospitals, followed by a focus group using a semi-structured interview guide. Results: Among 14 survey respondents, only 16.7% had completed a DNR-CF, despite all being familiar with the form. Main barriers included knowledge gaps, limited accessibility and uncertainty about responsibility. Focus group participants expressed concerns about the redundancy of completing the DNR-CFs in inpatient setting, the form’s validity overtime and medicolegal implications. Conclusion: Despite widespread familiarity with DNR-CFs, completion rates remain low due to systemic, provider-related, and ethical barriers. These findings raise ethical concerns about patient autonomy and potential for unwanted harms associated with resuscitative efforts. Strategies to address these challenges include improved provider education, clearer delineation of roles, and systemic support for GOC planning. Enhancing the completion of DNR-CFs can help ensure that patient wishes are respected, particularly in community emergencies, thereby upholding ethical standards in end-of-life 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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".