Creation of a theoretically rooted workbook to support implementers in the practice of knowledge translation
Bibliographic record
Abstract
BACKGROUND: Few training opportunities or resources for non-expert implementers focus on the "practice" as opposed to the "science" of knowledge translation (KT). As a guide for novice implementers, we present an open-access, fillable workbook combining KT theories, models, and frameworks (TMFs) that are commonly used to support the implementation of evidence-based practices. We describe the process of creating and operationalizing our workbook. METHODS: Our team has supported more than 1000 KT projects and 300 teams globally to implement evidence-based interventions. Our stakeholders have consistently highlighted their need for guidance on how to operationalize various KT TMFs to support novice implementers in "practising" KT. In direct response to these requests, we created a pragmatic, fillable KT workbook. The workbook was designed by KT scientists and experts in the fields of adult education, graphic design, and usability and was piloted with novice implementers. It is rooted in an integrated KT approach and applies an intersectionality lens, which prompts implementers to consider user needs in the design of implementation efforts. RESULTS: The workbook is framed according to the knowledge-to-action model and operationalizes each stage of the model using appropriate theories or frameworks. This approach removes guesswork in selecting appropriate TMFs to support implementation efforts. Implementers are prompted to complete fillable worksheets that are informed by the Theoretical Domains Framework, the Consolidated Framework for Implementation Research, the Behaviour Change Wheel, the Effective Practice and Organization of Care framework, Proctor's operationalization framework, the Durlak and DuPre process indicators, and the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework. As they complete the worksheets, users are guided to apply theoretically rooted approaches in planning the implementation and evaluation of their evidence-based practice. CONCLUSIONS: This workbook aims to support non-expert implementers to use KT TMFs to select and operationalize implementation strategies to facilitate the implementation of evidence-based practices. It provides an accessible option for novice implementers who wish to use KT methods to guide their work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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".