Integrated knowledge translation in population health intervention research: a case study of implementation and outcomes from a school-based project
Bibliographic record
Abstract
BACKGROUND: Integrated knowledge translation (IKT) is encouraged in population health intervention research (PHIR) to ensure the co-production of policy-relevant research, yet there is little published literature that reports its implementation and outcomes. The purpose of this study was to describe and evaluate the IKT approach used in a school-based PHIR project to understand how the research informed policy and practice and identify what influenced the IKT process. METHODS: A case study approach was used to provide an in-depth description of the IKT process and understand the co-production and application of research evidence. Data were collected through document review, a survey with all elementary school principals in the school board (n = 18) following dissemination of School Reports and interviews with the IKT research team (including two researchers and three knowledge users). RESULTS: Approximately half of the principals reported reading their School Report (52%) and almost all of these principals attributed the partial or full adoption, or implementation, of a new practice as a result of using the information (89%). Key themes related to the IKT process emerged across the interviews, including supportive relationships, role clarity, competing priorities and the complexities of population health interventions. CONCLUSIONS: The findings suggest that, while IKT can support policy and practice, it can be challenging to maintain engagement due to differing priorities and role ambiguity. Additional recognition, investment and research would enable better implementation of the approach, thereby bridging the gap between research, policy and practice.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.089 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".