Developing educational competencies for dissemination and implementation research training programs: an exploratory analysis using card sorts
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
BACKGROUND: With demand increasing for dissemination and implementation (D&I) training programs in the USA and other countries, more structured, competency-based, and tested curricula are needed to guide training programs. There are many benefits to the use of competencies in practice-based education such as the establishment of rigorous standards as well as providing an additional metrics for development and growth. As the first aim of a D&I training grant, an exploratory study was conducted to establish a new set of D&I competencies to guide training in D&I research. METHODS: Based upon existing D&I training literature, the leadership team compiled an initial list of competencies. The research team then engaged 16 additional colleagues in the area of D&I science to provide suggestions to the initial list. The competency list was then additionally narrowed to 43 unique competencies following feedback elicited from these D&I researchers. Three hundred additional D&I researchers were then invited via email to complete a card sort in which the list of competencies were sorted into three categories of experience levels. Participants had previous first-hand experience with D&I or knowledge translation training programs in the past. Participants reported their self-identified D&I expertise level as well as the country in which their home institution is located. A mean score was calculated for each competency based on their experience level categorization. From these mean scores, beginner-, intermediate-, and advanced-level tertiles were created for the competencies. RESULTS: The card sort request achieved a 41 % response rate (n = 124). The list of 43 competencies was organized into four broad domains and sorted based on their experience level score. Eleven competencies were classified into the "Beginner" category, 27 into "Intermediate," and 5 into "Advanced." CONCLUSIONS: Education and training developers can use this competency list to formalize future trainings in D&I research, create more evidence-informed curricula, and enable overall capacity building and accompanying metrics in the field of D&I training and research.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle