Reconceptualizing Integrated Knowledge Translation goals: a case study on basic and clinical science investigating the causes and consequences of food allergy
Bibliographic record
Abstract
BACKGROUND: Integrated Knowledge Translation (IKT) and other forms of research co-production are increasingly recognized as valuable approaches to knowledge creation as a way to better facilitate the implementation of scientific findings. However, the nature of some scientific work can preclude direct knowledge to action as a likely outcome. Do IKT approaches have value in such cases? METHODS: This study used a qualitative case study approach to better understand the function of IKT in a non-traditional application: basic and clinical science investigating the causes and consequences of food allergy. Building off previous baseline findings, data were obtained through in-depth interviews with project scientists and steering committee members and complemented by researcher observation. Data were analyzed through an integrated approach to understand how well participants perceived the stipulated project IKT outcomes had been met and to better understand the relationship between different forms of IKT goals, outcomes, and impacts. RESULTS: We propose a conceptual model which builds temporal continuity into the IKT work and understands success beyond truncated timelines of any one project. The model proposes project IKT goals be conceptualized through three metaphorical tower blocks: foundational (changing the culture for both scientists and knowledge-users), laying the groundwork (building relationships, networks and sparking scientific inquiry), and putting scientific knowledge to action. Based on this model, this case study demonstrated notable success at the foundational and intermediate blocks, though did not turn basic and clinical research knowledge into actionable outcomes within the project timespan. CONCLUSIONS: We find that current IKT literature which situates success as filling a knowledge to action gap is conceptually inadequate for understanding the full contributions of IKT activities. This work highlights the need for building cultural and scientific familiarity with IKT in order to better enable knowledge to action translation. Improving understanding and communication of science and empowering knowledge-users to engage with the research agenda are long-term strategies to build towards knowledge implementation and lay the ground work for many future research projects.
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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 | 0.087 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".