Understanding the time demands of integrated knowledge translation (iKT)
Bibliographic record
Abstract
College of Kinesiology Research Theme: Healthy aging and management of chronic conditions Introduction. Chronic pain is a public health emergency, affecting one in five Canadian adults. Without adequate pain management strategies, chronic pain can have detrimental effects on physical function, quality of life, and mental health. Despite strong evidence supporting physical activity as an effective nonpharmacological pain management strategy, most individuals who experience chronic pain are inactive. Psychosocial factors, such as fear of movement, pain anxiety, and low pain acceptance, contribute to inactivity. The Active Living for Pain (ALP) research team applied integrated knowledge translation (iKT) to co-develop, with patient and community partners, an accessible and acceptable physical activity program for adults living with moderate to severe chronic pain. The 6-week Movement That Matters (MTM) program targets the building of individuals' knowledge, confidence, and skills needed to engage in and maintain long-term physical activity participation. Purpose. The study purpose was to record the time required to engage in an iterative iKT approach in the co-development of MTM program materials and program logistics. Methods. The amount of time for ALP researchers and knowledge users to co-develop and finalize MTM materials (e.g., instructor implementation guide, participant habit tracker) and logistics (e.g., online MTM outcome surveys) was recorded. Knowledge users included patient partners and certified physical activity instructors. Results. The iKT process of co-development and finalization of the MTM materials ranged from 3 months (implementation guide) to 10 months (program overview guide and participant habit tracker). The time to finalize program logistics ranged from 6 weeks (securing of physical activity equipment) to 5 months (development and testing of online surveys, including participant screening, pre-program, end-program, and 1-month end-program surveys). Conclusion. The iKT process was time-intensive, requiring substantial coordination, collaboration, and iterative development between researchers and knowledge users. However, as recognized by the Canadian Institutes of Health Research, implementing iKT in program design has the potential to lead to more user-centred and effective programs in real-world settings. Researchers should be aware of the time required to meaningfully engage in iKT processes and account for this during program development.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".