Building capacity in dissemination and implementation research: the presence and impact of advice networks
Notice bibliographique
Résumé
BACKGROUND: As dissemination and implementation (D&I) research increases, we must continue to expand training capacity and research networks. Documenting, understanding, and enhancing advice networks identifies key connectors and areas where networks are less established. In 2012 Norton et al. mapped D&I science advice and collaboration networks. The current study builds on this work and aims to map current D&I research advice networks. METHODS: D&I researchers in the United States (US) and Canada were identified through a combination of publication metrics, and key persons identified networks and were invited to participate (n = 1,576). In this social network analysis study, participants completed an online survey identifying up to 10 people from whom they sought and/or gave advice on D&I research. Participants identified four types of advice received: research methods, grant, career, or another type (e.g., work/life balance). We used descriptive statistics to characterize the sample and network metrics and visualizations to describe the composition of advice networks. RESULTS: A total of 482 individuals completed the survey. Eighty-six (18%) worked in Canada and 396 (82%) in the US. Respondents had varying D&I research expertise levels; 14% beginner expertise, 45% intermediate, 29% advanced, and 12% expert. The advice network included 978 connected nodes/individuals. For all research types, out-degree, or advice giving, was higher for those with advanced or expert-level expertise (6.9 and 11.9, respectively) than those with beginner or intermediate expertise (0.8 and 2.2, respectively). Respondents reporting White race reported giving (out-degree = 5.2) and receiving (in-degree = 6.1) more advice compared to individuals reporting Asian (out-degree = 2.9, in-degree = 5.3), Black (out-degree = 2.3, in-degree = 5.2), or other races (out-degree = 2.5, in-degree = 5.4). Assortativity analyses revealed 98% of network ties came from individuals within the same country. The top two reasons for advice seeking were trusting the individual to give good advice (78%) and the individual's knowledge/experience in specific D&I content (69%). CONCLUSIONS: The D&I research network is becoming more dispersed as the field expands. Findings highlight opportunities to further connect D&I researchers in the US and Canada, individuals with emerging skills in D&I research, and minoritized racial groups. Expanding peer mentoring opportunities, especially for minoritized groups, can enhance the field's capacity for growth.
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Comment cette classification a été obtenuedéplier
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 | 0,019 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».