Can Hospital Doctors Provide Quality Palliative Care Informed by End-of-Life Care Legislation
Bibliographic record
Abstract
Background and Aim: Approximately 50% of deaths in Australia occur in hospitals, and this number is growing. Studies consistently show that doctors have poor knowledge of end-of-life decision making; however, this has not been examined in specific groups of hospital doctors. We examined hospital doctors' knowledge of key elements of end-of-life care legislation. Materials and Methods: We conducted a prospective, observational, cross-sectional study of doctors from a large Australian public tertiary health network using six questions formulated on basic key elements of the legislation. Demographic data collected included years of work experience, clinical unit, and proportion of work hours spent with dying patients. Results: Of the 201 doctors censored, senior doctors (>10 years' experience) were the least knowledgeable group. Only approximately 20% of doctors correctly answered all questions. Thirty-two percent would potentially provide futile treatment if demanded by a competent patient. Fifty percent did not know how to locate an advance directive in the hospital record. There was confusion regarding the role of the substitute decision-maker. Conclusions: Approximately a quarter of hospital doctors practise with a poor understanding of the law over the various domains. The urgent call for education is further highlighted not only for students and junior doctors but also for senior doctors who scored poorly. Educational efforts could begin from addressing the simple key areas of legislation covered in the survey.
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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 | 0.007 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".