Engaging Survivors of Critical Illness in Health Care Assessment and Policy Development. Ethical and Practical Complexities
Bibliographic record
Abstract
Health systems, granting agencies, and professional societies are increasingly involving patients and their family members in the delivery of health care and the improvement of health sciences. This is a laudable advance toward fully patient-centered medicine. However, patient engagement is not a simple matter, either practically or ethically. The complexities include (1) the physical limitations that patients and their family members may have, from traveling to meetings to special dietary needs; (2) the emotional sensitivities patients and their families might experience-from distress at discussions of disease prognosis, outcomes, and therapies to being inexperienced at public speaking; and (3) the fact that advocacy efforts by patients and family members, which may be encouraged at the national level, may threaten individual professionals providing care to individual patients and may result in risk to patients. In this article, a patient-physician and patient-bioethicist set out the obstacles, including ones that they have encountered in their own advocacy efforts. The aim is to survey the practical and ethical landscape so that solutions to various problems may be identified and solved as we move forward in our efforts to involve patients and their families in research, policy, and quality improvement in critical care medicine.
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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 | 0.184 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.011 | 0.019 |
| 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".