Managing ethical issues in patient care and the need for clinical ethics support
Bibliographic record
Abstract
Objective To investigate the range, frequency and management of ethical issues encountered by clinicians working in hospitals in New South Wales (NSW), Australia. Methods A cross-sectional survey was conducted of a convenience sample of 104 medical, nursing and allied health professionals in two NSW hospitals. Results Some respondents did not provide data for some questions, therefore the denominator is less than 105 for some items. Sixty-two (62/104; 60%) respondents reported occasionally to often having ethical concerns. Forty-six (46/105; 44%) reported often to occasionally having legal concerns. The three most common responses to concerns were: talking to colleagues (96/105; 91%); raising the issue in a group forum (68/105; 65%); and consulting a relevant guideline (64/105; 61%). Most respondents were highly (65/99; 66%) or moderately (33/99; 33%) satisfied with the ethical environment of the hospital. Twenty-two (22/98; 22%) were highly satisfied with the ethical environment of their department and 74 (74/98; 76%) were moderately satisfied. Most (72/105; 69%) respondents indicated that additional support in dealing with ethical issues would be helpful. Conclusion Clinicians reported frequently experiencing ethical and legal uncertainty and concern. They usually managed this by talking with colleagues. Although this approach was considered adequate, and the ethics of their hospital was reported to be satisfactory, most respondents indicated that additional assistance with ethical and legal concerns would be helpful. Clinical ethics support should be a priority of public hospitals in NSW and elsewhere in Australia. What is known about the topic? Clinicians working in hospitals in the US, Canada and UK have access to ethics expertise to help them manage ethical issues that arise in patient care. How Australian clinicians currently manage the ethical issues they face has not been investigated. What does this paper add? This paper describes the types of ethical issues faced by Australian clinicians, how they manage these issues and whether they think ethics support would be helpful. What are the implications for practitioners? Clinicians frequently encounter ethically and legally difficult decisions and want additional ethics support. Helping clinicians to provide ethically sound patient care should be a priority of public hospitals in NSW and elsewhere in Australia.
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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 | 0.030 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".