A mapping review and critique of the literature on translation, dissemination, and implementation capacity building initiatives for different audiences
Bibliographic record
Abstract
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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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 | 0.143 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.042 | 0.044 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".