A mapping review and critique of the literature on translation, dissemination, and implementation capacity building initiatives for different audiences
Notice bibliographique
Résumé
BACKGROUND: Capacity building is critical for research and practice as the fields of dissemination, implementation and translation science continue to grow. Some scholars state that capacity building should be grounded in competencies. However, the fields are unclear in determining which competencies are relevant for whom, including the content and appropriate level of information and skills for different roles. The goal of this study was to catalogue competencies across current D&I capacity building initiatives. METHODS: We conducted a mapping review to examine to what extent are theories or frameworks used to guide capacity building, who is being trained, to what extent do capacity building initiatives include a health equity focus, which competencies are being outlined or suggested, how are they being defined, and whether the competencies can be organized along different roles of participants. As a mapping review, we broadly searched for papers using the keywords "training D&I" OR "training implementation" OR "training translation" OR "training dissemination" and included debate and empirical papers about capacity building initiatives in the sample. RESULTS: A total of 42 articles (from 2011 to 2024) were reviewed, including training development and/or evaluation (n = 25) and conceptual (n = 17) articles. Of the training articles, 13 (52%) specified a framework that guided training. Participants in training included graduate students, researchers, practitioners, and mixed audiences. Fourteen (56%) of the trainings were conducted in the USA, seven (28%) in Canada and other countries. The length of training ranged from two days to two years. Four trainings had an explicit focus on equity. A total of 307 unique competencies were identified and divided into themes: Knowledge, Skills, Engagement with Other Disciplines, Equity, Attitude and Relational Aspects, Capacity Building, Quality Improvement, and Mentorship. CONCLUSIONS: While there are many D&I capacity building initiatives, we found little consistency in competencies that guided training activities for diverse audiences. Few training activities explicitly identified guiding theories or frameworks or tailored competencies toward different levels of interest in D&I research. Even fewer had an explicit focus on health equity. As the fields continue to foster capacity building programs, it will be important to think critically about the types of competencies we are developing for whom, how, and why.
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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 | 0,143 | 0,314 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,042 | 0,044 |
| Études des sciences et des technologies | 0,004 | 0,008 |
| Communication savante | 0,013 | 0,018 |
| Science ouverte | 0,005 | 0,008 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».