Developing Capacity in Dissemination and Implementation Research in the Eastern Mediterranean Region: Evaluation of a Training Workshop
Bibliographic record
Abstract
As the demand for dissemination and implementation (D&I) research grows globally, there is a need for D&I capacity building in regions where D&I science is underrepresented. The Workshop on Dissemination and Implementation Research in Health (WONDIRH) was aimed for participants in the Eastern Mediterranean region to (1) appreciate the complex process of bridging research and practice in a variety of real-world settings, and (2) develop research that balances rigor with relevance and employs study designs and methods appropriate for the complex processes involved in D&I. The present exploratory study investigates participants' satisfaction with the workshop, the enhancement of their self-rated confidence in D&I skills, as well as their intention to apply the learned content into practice. The workshop included four weekly 90-min virtual interactive training sessions in conjunction with open access content from the National Cancer Institute Training Institute in Implementation and Dissemination Research in Cancer (TIDIRC). We applied a one-group pre-post design for the evaluation of workshop. Participants were invited to self-rate their confidence in D&I competencies (15 items, pre and post workshop). At the end of the workshop, participants additionally were asked to rate their satisfaction (5 items, 1-5 scales), and their intention to apply the learned content into practice (4 items, 1-5 scales). Of the 77 workshop participants, 34 completed the evaluation. Confidence improved between pre- and post-workshop assessments in all 15 self-rated D&I competencies. Respondents were generally satisfied with the workshop (mean satisfaction range 3.82-4.26 across the 5 items) and endorsed intentions to apply workshop topics (mean intention range 4.03-4.35 across the 4 items). This initial workshop demonstrated the ability to attract and engage participants to enhance their confidence in D&I research competencies and skills and to build capacity in D&I research. Future efforts should consider offering targeted training for researchers at different stages and to clearly articulate learning objectives. Supplementary Information: The online version contains supplementary material available at 10.1007/s43477-022-00067-y.
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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 | 0.172 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".