A systematic review of dissemination and implementation science capacity building programs around the globe
Bibliographic record
Abstract
BACKGROUND: Research centers and programs focused on dissemination and implementation science (DIS) training, mentorship, and capacity building have proliferated in recent years. There has yet to be a comprehensive inventory of DIS capacity building program (CBP) cataloging information about activities, infrastructure, and priorities as well as opportunities for shared resources, collaboration, and growth. The purpose of this systematic review is to provide the first inventory of DIS CBPs and describe their key features and offerings. METHODS: We defined DIS CBPs as organizations or groups with an explicit focus on building practical knowledge and skills to conduct DIS for health promotion. CBPs were included if they had at least one capacity building activity other than educational coursework or training alone. A multi-method strategy was used to identify DIS CBPs. Data about the characteristics of DIS CBPs were abstracted from each program's website. In addition, a survey instrument was developed and fielded to gather in-depth information about the structure, activities, and resources of each CBP. RESULTS: In total, 165 DIS CBPs met our inclusion criteria and were included in the final CBP inventory. Of these, 68% are affiliated with a United States (US) institution and 32% are internationally based. There was one CBP identified in a low- and middle-income country (LMIC). Of the US-affiliated CBPs, 55% are embedded within a Clinical and Translational Science Award program. Eighty-seven CBPs (53%) responded to a follow-up survey. Of those who completed a survey, the majority used multiple DIS capacity building activities with the most popular being Training and Education (n=69, 79%) followed by Mentorship (n=58, 67%), provision of DIS Resources and Tools (n=57, 66%), Consultation (n=58, 67%), Professional Networking (n=54, 62%), Technical Assistance (n=46, 52%), and Grant Development Support (n=45, 52%). CONCLUSIONS: To our knowledge, this is the first study to catalog DIS programs and synthesize learnings into a set of priorities and sustainment strategies to support DIS capacity building efforts. There is a need for formal certification, accessible options for learners in LMICs, opportunities for practitioners, and opportunities for mid/later stage researchers. Similarly, harmonized measures of reporting and evaluation would facilitate targeted cross-program comparison and collaboration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.202 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.027 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".