Notice bibliographique
Résumé
We would like to thank Brisson and colleagues1 for their letter in reply to our Commentary. Clearly, we agree on the learning potential of patient tracking in the electronic health record (EHR) and that the primacy of a patient’s right to privacy (both in the present and future) and our obligation of confidentiality should not be com promised in the pursuit of learning. However, it is still unclear to us why they feel that distinct guidelines are required to guide behavior when a specific group (medical students) accesses a particular source of data (EHR) at a certain time point (in the future). When students begin training we anticipate that their clinical skills will not be commensurate with those of a practicing physician, but we do not have differential expectations on confidentiality and professionalism.2 Having distinct guidelines to guide the ethical behavior of medical students implies distinct expectations, which is not the case. When a patient confides in a physician or physician-in-training, this information can be stored in various locations, including an EHR, paper charts, and the long-term memory of the physician/trainee. Having distinct guidelines implies privacy risk for electronic data that is somehow absent for data stored elsewhere. Admittedly, the privacy risk of electronic data may be different (remote access may increase risk, whereas the ability to password protect data and identify users might reduce the risk)—but privacy risk is not unique to data stored electronically. Brisson and colleagues raise the possibility that by accessing future events, students may uncover sensitive information. But this risk is not unique to students or to the future—and whether or not this constitutes “snooping” depends upon motivation. Most of us have experienced a situation where we inadvertently discovered sensitive information that was not relevant to our clinical task. Irrespective of how and when we acquire these data, there is the same expectation of privacy and confidentiality. If we were to access data with the intention of uncovering sensitive information that had no clinical or educational merit, then we would be guilty of snooping—which is clearly unethical, regardless of the source of data or our level of training. When completing clinical rotations we sign over patients for whom the diagnosis has not yet been made and/or the response to treatment established. From our own experience, and in discussion with our colleagues, it is common practice to ruminate on cases, ask colleagues for updates, and review progress via paper and electronic charts. Rather than snooping, the primary driver of information-seeking behavior is our need for cognition.3,4 And, while we appreciate that individuals may behave differently when online versus in person,5 we should still be capable of meeting our learning needs without compromising the privacy needs of patients. Kevin McLaughlin, MB ChB (Hons), PhD Assistant dean of undergraduate medical education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; [email protected] Sylvain Coderre, MD, MSc Associate dean of undergraduate medical education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,115 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,007 | 0,010 |
| Science ouverte | 0,006 | 0,005 |
| Intégrité de la recherche | 0,073 | 0,089 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».