Divulgation et encadrement du professionnel de la santé séropositif : les leçons de l'affaire Di Lorenzo
Bibliographic record
Abstract
Résumé: Une problématique est au coeur du présent essai: l'obligation pour un professionnel de la santé de divulguer sa séropositivité à son patient. Faisant suite à l'affaire Maria Di Lorenzo, cette chirurgienne séropositive du CHU Ste-Justine à Montréal qui a opéré 2 614 patients durant une période d'environ 13 ans, certaines réflexions tant au niveau juridique, déontologique, qu'au niveau éthique et moral, ont été soulevées au Québec et ce, avec raison. Nous exposerons donc dans un premier temps, les faits connus du cas du Dre Di Lorenzo pour ensuite établir le cadre normatif entourant plus particulièrement la problématique de la divulgation. Ensuite, les règles déontologiques en matière de responsabilité professionnelle, les règles législatives et jurisprudentielles en matière de responsabilité civile médicale et hospitalière ainsi que le profil historique de lignes directrices provenant des États-Unis, puis établies au Canada et au Québec en matière de divulgation et de précautions à prendre par le professionnel de la santé infecté, seront énoncés. Nous terminerons finalement par un exposé de divers enjeux éthiques en faveur et en défaveur de la divulgation qui nous permettra de résumer les arguments de tous niveaux à l'égard de la problématique principale et ainsi, d'en tirer nos conclusions.||Abstract: One issue is at the heart of the present essay: the obligation for an HIV posittve health professional to disclose his condition to his patient. Following the case of Maria Di Lorenzo, this HIV positive surgeon of the CHU Ste-Justine in Montreal which operated 2 614 patients over a period of approximately 13 years, certain considerations were justifiably raised on a legal, deontological and ethical and moral point of view. First, we will establish the known facts of the case of Dr Di Lorenzo and we will follow by establishing the standards surrounding, more particularly, the issue of disclosure. Secondly, the deontological rules in matters of professional liability, the legal and jurisprudential rules in matters of civil liability of a doctor and of an hospital together with the recommendations issued in the United States, and established in Canada and in Quebec in matters of disclosure and of universal precautions to be taken by an HIV positive health-care professional, will be established. To conclude, we will expose various ethical stakes for and against the disclosure, which will permit us to summarize the arguments on all levels re garding the main issue of the present essay and therefore, submit our conclusions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".