Mapping training needs for dissemination and implementation research: lessons from a synthesis of existing D&I research training programs
Bibliographic record
Abstract
With recent growth in the field of dissemination and implementation (D&I) research, multiple training programs have been developed to build capacity, including summer training institutes, graduate courses, degree programs, workshops, and conferences. While opportunities for D&I research training have expanded, course organizers acknowledge that available slots are insufficient to meet demand within the scientific and practitioner community. In addition, individual programs have struggled to best fit various needs of trainees, sometimes splitting coursework between specific D&I content and more introductory grant writing material. This article, stemming from a 2013 NIH workshop, reviews experiences across multiple training programs to align training needs, career stage and role, and availability of programs. We briefly review D&I needs and opportunities by career stage and role, discuss variations among existing training programs in format, mentoring relationships, and other characteristics, identify challenges of mapping needs of trainees to programs, and present recommendations for future D&I research training.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.226 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".