L’efficacité de l’encadrement du secours par le médecin en dehors de ses fonctions : analyse du droit québécois à la lumière du droit comparé
Bibliographic record
Abstract
While coming to the aid of a person in distress (termed “rescue” in some jurisdictions) is morally commendable, the relevance of transforming this moral duty into a legal one has long been at the center of a heated debate. Despite abundant literature on the topic, in Québec and elsewhere, the issue has never been resolved. The arguments put forward are mostly of a philosophical nature. It seemed desirable to evaluate the actual impact of the legal framework applicable to doctors offering assistance outside the realm of their normal practice in order to bring the debate to more utilitarian and comparable elements. Thus, we aimed to compare the efficiency of three categories of legal rules intended to encourage off-duty physicians to act as Good Samaritans, in order to identify potential solutions to increase efficiency. A first category encompasses legal rules which impose a duty to come to the aid of a person; a second category, rules which grant immunity from civil liability; and a third category, rules which allow physicians compensation for assistance they provide. Quebec civil law integrates all three and will be the focus of this study. Its legal solutions as they relate to off-duty medical assistance will be the object of a comparative study, through a selective analysis of rules which have emerged elsewhere in Canada, the United States, the United Kingdom, as well as in France, regarding various aspects of this issue.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".