Knowledge user perspectives on integrated knowledge translation (iKT) in health interventions research for people with multiple sclerosis: a qualitative descriptive study
Bibliographic record
Abstract
Abstract Background Integrated knowledge translation (iKT) represents an approach to optimizing health interventions research through active collaboration between researchers and knowledge users throughout the research process. To date, few studies have explored the process of engaging in iKT, particularly in the context of multiple sclerosis (MS) research. Building on a larger iKT-informed study exploring mindfulness-based interventions for people living with MS, this study explores the perspectives of iKT panellists and extended collaborators on the use of iKT in health research. Methods This qualitative descriptive study utilized one-on-one semi-structured interviews conducted using Zoom or Microsoft Teams. Interviews were 20–30 min in duration. An interview guide informed by the Ontario Brain Institute’s framework for patient engagement across the stages of research was used. Interviews were transcribed verbatim, coded, and analyzed using inductive thematic analysis. Results A total of eight iKT partners were interviewed, five were members of the iKT panel and three were extended collaborators. Five themes on the use of iKT in health interventions research on MS were identified: (1) defining iKT, (2) motivation and meaningful participation in iKT, (3) the importance of networking in iKT, (4) balancing multiple perspectives, and (5) barriers and facilitators to engaging in iKT. Within these themes, interviewees highlighted the need for further definition and operationalization of concepts. Discussion on the representativeness of iKT partners and recruitment of ‘hard to reach’ knowledge users was also salient. Conclusion The findings from this study provide useful considerations for other teams using an iKT approach. Future research directions include finding/maximizing meaningful ways for knowledge users to participate, exploring ways in which knowledge users could lead/co-lead (rather than consult on) research activities, and examining the potential role of an iKT facilitator.
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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 | 0.051 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".