Intellectual Virtue Vaccination Schedules Need to Include Boosters
Notice bibliographique
Résumé
To the Editor: Dr. Ahmadi Nasab Emran1 recently presented a unique approach to the issue concerning physician–pharmaceutical industry interactions. He calls for the development of intellectual virtues early in medical education in order to “vaccinate” against industry influence. This concept has broad implications for how medical information and treatment decisions are translated and shared, but it may not go far enough. With many vaccines, defense against an infection is not guaranteed, but the vaccination nonetheless provides a significant level of protection. In certain cases, the protection offered by the vaccine changes as the environment changes, necessitating updated versions (i.e., booster vaccinations) based on best predictions. Similarly, within medical education, for the development of intellectual virtues to provide the greatest degree of success in protecting against unreliable or biased interpretation of information, programs need to continually update education approaches based on the inevitably changing educational, technological, and informational environment. The core content of teaching in medical education will go a long way to contribute to this inoculation of intellectual virtues. However, the culture within institutions and programs must be such that these fundamentals are supported and fostered. This must go as deep as to view these virtues as essential characteristics addressed in the process of hiring faculty and educators within these programs such that each clinical exposure is consistently reflective of these virtues. Although we desire that virtues be in grained and lifelong, they will potentially fade or become diluted over time. Consid ering the fast-paced practices in which many medical learners will find themselves immersed in the future, inappropriate default behaviors developed peripheral to and during medical education and training can resurface when accessing, evaluating, and translating medical information for clinical care decisions. The need for ongoing “booster vaccinations” for intellectual virtues appears essential. This process needs to begin during medical education by engaging learners in discussions about these virtues and modeling practical ways to maintain them through their careers. The current context of popular continuing professional development programs in corporates relatively little in the way of intellectual virtues. Boosters, however, can be found in a variety of ways through intentionally seeking out the evolving array of high-quality non-industry-sponsored practice conferences, establishing formal faculty mentorships allowing for regular regrounding in evidence-informed practice, and utilizing office-based counter-detailing programs as an alternative to direct industry-sponsored presentations. Seeking out these and other innovative professional development strategies will help to ensure that intellectual virtue maintenance is a reality for future clinicians. Jamison Falk, PharmD Assistant professor, College of Pharmacy and Faculty of Health Sciences, and clinical pharmacotherapy specialist, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada; [email protected]
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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 | 0,007 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,012 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,008 |
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 ».