Fluency—Not Competency or Expertise—Is Needed to Incorporate Evidence Into Practice
Notice bibliographique
Résumé
Twenty years ago, the Journal of the American Medical Association published an article describing a “new approach to teaching the practice of medicine”: evidence-based medicine (EBM).1 Today, the ability to apply research to practice is a program accreditation requirement at both the undergraduate and postgraduate levels in the United States and Canada as well as an ongoing practice competency requirement. The EBM process—assessing the patient, asking the question, acquiring the evidence, assessing and applying that evidence, and finally evaluating the whole process—seeks to provide a framework for the integration of evidence into clinical practice. Much of the training around EBM has focused on building expertise in literature searching and critical appraisal of resources; however, as Moore’s2 response to the 2011 Question of the Year indicated, these efforts have not been successful in practice. Perhaps one of the most effective ways to ensure that those who work and learn in medical schools and teaching hospitals can develop to their full potential is to shift from the perspective that these individuals must become experts in EBM to the view that they should become comfortable with the tools which will allow them to be fluent users of the evidence. To that end, we propose four levels of performance: Literacy—knowing and understanding the EBM concepts; Competency—being able to apply these concepts in controlled conditions; Fluency—having a comfort level with incorporating the concepts into daily practice; and Expertise—having the high level of skill needed to create and demonstrate the tools that translate research into practice. There is simplicity with building competency around a skill set that can be defined, taught, and evaluated through assignments, objective structured clinical examinations, or licensing examinations. But what is required to move a medical student or physician from a level of competency to one of fluency? Increasing their understanding of clinical information systems, both organizational and technological, is key. Building on Marcum’s3 position that fluency can only be achieved in the workplace, the focus must be on providing training and support for processes that can be readily integrated into regular practice routines. For medical students, this would include determining realistic competencies for the clinical practice environment and simulating that environment to train them to integrate the skills and clinical tools and resources required. Just as fluency in language is best developed through immersion in the native culture, fluency in the use of health information is best developed through immersion in the culture of EBM. Of particular importance is the identification and support of physician role models. For residents and clinicians to become fluent, it is necessary to integrate clinical practice tools and resources into the clinical interface and to ensure that ongoing support and training are available at the point of need. Librarians and informaticians play crucial roles in fostering EBM fluency in medical schools and teaching hospitals. The ideal, as outlined by Moore,2 is the integration of these specialists into health care teams. When this is not possible due to issues of availability, scalability, or sustainability, a level of self-sufficiency is required on the part of health care practitioners. In conjunction with physicians and informaticians, librarians are developing and supporting health information technology initiatives that integrate point-of-care resources into the clinical interface. Librarians are responsible for ensuring that point-of-care resources are licensed and linked at as granular a level as possible, as well as for providing ongoing support and training in the use of those resources. They also play a major role in building medical students’ EBM skills, particularly those related to the effective search for and use of high-quality, best-evidence resources. Librarians must collaborate with medical educators and informaticians in the development and utilization of simulated clinical interfaces to present patient data and to integrate best-evidence resources into those interfaces. When this is accomplished, EBM skills will come to be seen as a seamless part of the clinical process, with a resultant increase in students’ confidence and fluency. While the effective implementation of electronic health records and clinical information systems is challenging, using technology to deliver and integrate EBM resources is the best chance of attaining Garrity’s4 vision of focusing on “evidence” as part of treatment and care to ensure that “clinicians are given just the information they need to advance health, to provide each person the best care, at the right time, every time.”
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 | 0,050 | 0,149 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,013 |
| Communication savante | 0,011 | 0,018 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,005 |
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 ».