Bibliographic record
Abstract
A health region, with multiple hospitals and community healthcare organizations, is faced with increased pressures to improve the ethical care of patients and improve staff experience across the system. Currently the patient satisfaction scores at many of the sites are quite low and recent Health Commission inspections in some hospitals have highlighted management of consent issues and patient-centered care as areas of major concern. The staff 's morale is waning and moral distress seems to be increasing. The CEO of the Strategic Health Authority believes that clinical ethics could potentially make a significant difference to the overall culture of the system but feels that the existing mechanisms are not that effective. She begins to consult with experts in the field to discuss how clinical ethics can help her to improve her health system. “ABC Health Care” has an established clinical ethics program that performs a variety of functions including case consultation, education, policy work, and scholarly writing. Although ABC has received positive accreditation ratings relating to clinical ethics, many within ABC – including both administrators and clinical staff – have a general sense that ABC's current clinical ethics program may not be fully addressing the organization's needs. For example, the program tends to focus on a narrow range of ethical concerns, mostly related to high-profile acute situations in the intensive care and emergency units. In contrast, staff experience a much broader range of ethical issues in their work day to day, and many issues and areas go unserved. Although the clinical ethics program devotes many hours to ethics consultation, similar ethical issues continue to recur again and again.[…]
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.081 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".