Mentored training and its association with dissemination and implementation research output: a quasi-experimental evaluation
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
BACKGROUND: There is a continued need to evaluate training programs in dissemination and implementation (D&I) research. Scientific products yielded from trainees are an important and objective measure to understand the capacity growth within the D&I field. This study evaluates our mentored training program in terms of scientific productivity among applicants. METHODS: Post-doctoral and early-career cancer researchers were recruited and applied to the R25 Mentored Training for Dissemination and Implementation Research in Cancer (MT-DIRC) between 2014 and 2017. Using application details and publicly available bibliometric and funding data, we compared selected fellows with unsuccessful applicants (nonfellows). We extracted Scopus citations and US federal grant funding records for all applicants (N = 102). Funding and publication abstracts were de-identified and coded for D&I focus and aggregated to the applicant level for analysis. Logistic regression models were explored separately for the odds of (1) a D&I publication and (2) US federal grant funding post year of application among fellows (N = 55) and nonfellows (N = 47). Additional models were constructed to include independent variables that attenuated the program's association by 5% or more. Only US-based applicants (N = 87) were included in the grant funding analysis. RESULTS: Fellows and nonfellows were similar across several demographic characteristics. Fellows were more than 3 times more likely than nonfellows to have grant funding after MT-DIRC application year (OR 3.2; 95% CI 1.1-11.0) while controlling for time since application year; the association estimate was 3.1 (95% CI 0.98-11.0) after adjusting for both cancer research area and previous grant funding. For publications, fellows were almost 4 times more likely to publish D&I-focused work adjusting for time (OR 3.8; 95% CI 1.7-9.0). This association lessened after adjusting for previous D&I publication and years since undergraduate degree (OR 2.9; 95% CI 1.2-7.5). CONCLUSIONS: We document the association of a mentored training approach with built-in networks of peers to yield productive D&I researchers. Future evaluation efforts could be expanded to include other forms of longer-term productivity such as policy or practice change as additional objective measures. D&I research trainings in the USA and internationally should consider common evaluation measures.
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.
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 | 0,009 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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