The Use of the Delphi and Other Consensus Group Methods in Medical Education
Notice bibliographique
Résumé
Purpose: The Delphi and other consensus methods are a systematic means to measure and develop consensus when empirical evidence is lacking or contradictory. They aim to determine the extent to which experts agree about a particular issue, with the ultimate goal of providing a unified expert opinion. In medical education, there are several important areas of inquiry that are plagued by high levels of uncertainty and limited evidence-based literature. Consequently, consensus group methods are relevant to medical educators. Despite extensive use in other fields, consensus methods are poorly standardized and inconsistently described. Several articles highlight significant deficiencies in methodology and reporting.1,2 Given these deficiencies, the following four questions are addressed: (1) How extensively are consensus methods used in medical education research? (2) What types of methods are used? (3) For what purpose? (4) Is there standardization in the application and reporting of the methods? Method: MEDLINE, Embase, PsycINFO, PubMed, Scopus, and ERIC databases were searched for articles focusing on medical education and using the following keywords: “Delphi,” “RAND,” “nominal group,” and “consensus group methods” (2009–2013). Inclusion criteria included English-language and full-text articles of completed research. A standardized extraction form was developed to evaluate the methodology and quality of reporting. Through an iterative process the form and definitions were refined. The final data extraction form consisted of two parts: (1) a section to gather demographic information, such as type of consensus group used and purpose of the project; and (2) specific features reflecting methodological rigor, such as reporting of literature review, number of participants in each round, type of feedback provided, and definition of consensus. Results: The initial search yielded 692 articles. After removal of duplicates, 143 full-text articles met inclusion criteria. Based on previous reviews, this number was deemed to be a sufficiently representative sample. The consensus methods described were the Delphi (40.6%), modified Delphi (31.5%), nominal group technique (NGT) (11.2%), and various other combinations (e.g., Delphi and NGT) (16.7%). The most common purposes were for curricular development or renewal (25.9%), assessment tool development (21%), and defining competencies (10.5%). The quality of reporting was variable; 107/143 (66.4%) described that a literature review was conducted in preparation for the questionnaire, 36/143 (25.2%) described what background information was provided to participants, 93/143 (65%) provided the response rates, 59/143 (41.3%) reported if private decisions were collected, 50/143 (35%) described formal feedback of group ratings, and 48/143 (33.6%) defined consensus a priori. Conclusions: This study of consensus group methods used in the medical education literature highlights the considerable variability in reporting. Studies do not consistently provide sufficient detail about methods, thus leading to a lack of scientific credibility. If consensus methods should inform best education practice, they must be rigorously conducted.
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 | 0,515 | 0,571 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,032 | 0,024 |
| Études des sciences et des technologies | 0,007 | 0,017 |
| Communication savante | 0,011 | 0,015 |
| Science ouverte | 0,006 | 0,020 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».