Knowledge translation for realist reviews: A participatory approach for a review on scaling up complex interventions
Bibliographic record
Abstract
Background: Knowledge syntheses that use a realist methodology are gaining popularity. Yet, there are few reports in the literature that describe how results are summarised, shared and used. This paper aims to inform knowledge translation (KT) for realist reviews by describing the process of developing a KT strategy for a review on pathways for scaling up complex public health interventions. Methods: The participatory approach used for the realist review was also used to develop the KT strategy. The approach included three main steps, namely (1) an international meeting focused on interpreting preliminary findings from the realist review and seeking input on KT activities; (2) a targeted literature review on KT for realist reviews; and (3) consultations with primary knowledge users of the review. Results: The international meeting identified a general preference among knowledge users for findings from the review that are action oriented. A need was also identified for understanding how to tailor findings for specific knowledge user groups in relation to their needs. The literature review identified four papers that included brief descriptions of planned or actual KT activities for specific research studies; however, information was minimal on what KT activities or products work for whom, under what conditions and why. The consultations revealed that KT for realist reviews should consider the following: (1) activities closely aligned with the preferences of specific knowledge user groups; (2) key findings that are sensitive to factors within the knowledge user's context; and (3) actionable statements that can advance KT goals, activities or products. The KT strategy derived from the three activities includes a planning framework and tailored KT activities that address preferences of knowledge users for findings that are action oriented and context relevant. Conclusions: This paper provides an example of a KT strategy for realist reviews that blends theoretical and practical insights. Evaluation of the strategy's implementation will provide useful insights on its effectiveness and potential for broader application.
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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 | 0.708 | 0.747 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.036 | 0.021 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".