Assurer la protection des renseignements personnels en recherche dans les départements de pharmacie
Bibliographic record
Abstract
Resume Objectif : L’objectif de cet article est de faire le point sur la protection des renseignements personnels en recherche clinique et evaluative et de determiner les actions requises pour assurer la confidentialite dans le cadre de la recherche en etablissement de sante. Description de la problematique : Malgre un effort important pour assurer la confidentialite, il existe souvent des lacunes sur le plan de la protection des renseignements personnels dans le cadre d’activites de recherche. Discussion : Inspiree des dix pratiques exemplaires en matiere de protection de la vie privee dans la recherche en sante proposes par les Instituts de recherche en sante du Canada, une liste de 32 actions pertinentes aux activites de recherche d’un departement de pharmacie a ete elaboree, notamment en ce qui a trait a la collecte, au partage et a la conservation de donnees. De plus, 17 exemples de pratiques non exemplaires et fictives illustrent l’importance d’etablir des procedures en ce qui a trait a la protection des renseignements personnels. Conclusion : Tout pharmacien engage dans des activites de recherche devrait prendre le temps de se questionner et d’elaborer des procedures assurant la confidentialite des donnees personnelles des patients avant d’entreprendre un nouveau projet de recherche. Abstract Objective: The objective of this article is to consider protection of personal information in clinical and evaluative research and to determine the actions required to ensure confidentiality in research in healthcare establishments. Problem description: Despite significant efforts to ensure confidentiality, there exist many gaps in regard to protection of personal information in the context of research activities. Discussion: Guided by the ten best practices regarding privacy in health research developed by the Canadian Institutes of Health Research, a list of 32 actions relevant to the research activities of a pharmacy department was developed, mainly in regard to data collection, sharing, and storing. In addition, 17 examples of fictive and non-exemplary practices demonstrate the importance of implementing procedures for the protection of personal information. Conclusion: Any pharmacist involved in research activities should take the time to question and develop procedures that ensure the confidentiality of patient personal data, and this prior to starting a new research project. Key words: confidentiality; ethics; safety; corrective measures; best practices
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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 | 0.072 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".