How does integrated knowledge translation work? A realist review
Bibliographic record
Abstract
BACKGROUND: Integrated knowledge translation (IKT), or research co-production, is a research approach where researchers and knowledge users carry out a study as equal partners. A growing evidence base demonstrates that IKT produces research findings that are useful, usable and used. Despite knowing how IKT works, we have yet to ascertain how it operates. We conducted a realist review to examine the key mechanisms thought to explain how IKT approaches work in relation to the generation of research in the healthcare sector. METHODS: The research question was the following: what are the necessary conditions (context) and key mechanisms that explain the success of IKT in the healthcare sector? We conducted the review in two phases. During phase 1, we collaborated with knowledge users and scoped the literature to develop preliminary program theories. In phase 2, we inductively tested the preliminary program theories against the literature. We searched OVID Medline, Embase, PsycINFO, the Cumulative Index of Nursing and Allied Health Literature, Social Sciences Abstracts, and ABI Inform for empirical articles published between 2002 and 2017. An updated search included Embase and OVID Medline articles published between 2017 and 2020. The review includes 84 papers. We included articles written in English that focused on the health sector; encompassed the co-generation of research with researchers, policy-makers, administrators and/or practitioners; and evaluated the IKT approach. In analysing the retained articles, we produced three program theories by looking for common patterns and challenging and refining these theories. RESULTS: We postulate three program theories about how teams of researchers and knowledge users work to generate research. We identified three important conditions: infrastructure, role clarity and power sharing. Under particular infrastructure arrangements, effective partnerships are mechanisms that lead to the production of research findings relevant to knowledge users. Role clarity also triggers effective partnerships. With power sharing, synergy is a mechanism that leads to the use of findings. CONCLUSIONS: We identified different conditions (contextual factors), including infrastructure, role clarity and power sharing, in which IKT produces research findings that are of relevance to knowledge users and used in health settings. Effective partnerships are necessary but an insufficient mechanism for actual use of research findings, and must occur before the mechanism of partnership synergy. This work contributes to our understanding of how to enhance the uptake of evidence by presenting three program theories and a consolidated, mid-range theory of IKT.
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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: Review About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.063 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| 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, 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".