What Do You Do When You Can Do No More? Limited Resources, Unimaginable Environments, Personal Danger: What Have Previous Disasters Taught Us About Moral and Ethical Challenges?
Bibliographic record
Abstract
Historically, natural and manmade disasters create many victims and impose pressures on health-care infrastructure and staff; potentially hampering the provision of patient care and overloading clinician capacity. Throughout the course of history, clinicians have performed heroics to work well above their required duty, despite limitations, even putting their own health and safety at risk. In times when clinicians needed to either physically abandon patients or consider abandoning active treatment, we have seen extreme hesitancy to do so, fearing that they may be giving up too soon, that undue harm may come to patients, or even feeling unsure of legal or moral burdens that may ensue. In times when clinicians are placed in this unimaginable position, feeling isolated and overwhelmed, it is essential that they be supported and provided with resources to standardize decision-making.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".