Ethical Dilemmas with Little Time for Reflection: A Discussion of the Ethics of Out-of-Hospital Refusals
Bibliographic record
Abstract
In this article, we consider an approach for ethical decision-making for refusals in the out-of-hospital environment. Autonomy and beneficence are discussed as the two ethical principles central to guiding paramedic decision-making in this context. We describe some situations where the two principles may come into conflict and where the working paramedic may be faced with an ethical dilemma. These cases may involve temptations of medical paternalism, which we argue ought to be avoided if possible. A discussion on navigating between autonomy and beneficence will be presented in order to help paramedics sort through dilemmas when these principles conflict. We argue that when these principles are in conflict, autonomy should primarily be respected – however, we will examine situations where the principle of autonomy cannot be applied and the paramedic should either attempt to rectify the patient's capacity for autonomous decision-making, or, if not possible, proceed with the principle of beneficence.
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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 | 0.082 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.100 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.036 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 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".