Variable participation of knowledge users in cancer health services research: results of a multiple case study
Bibliographic record
Abstract
BACKGROUND: Integrated knowledge translation (IKT) is a research approach in which knowledge users (KUs) co-produce research. The rationale for IKT is that it leads to research that is more relevant and useful to KUs, thereby accelerating uptake of findings. The aim of the current study was to evaluate IKT activities within a cancer health services research network in Ontario, Canada. METHODS: An embedded multiple case study design was used. The cases were 5 individual studies within an overarching cancer health services research network. These studies focused on one of the following topics: case costing of cancer treatment, lung cancer surgery policy analysis, patient and provider-reported outcomes, colorectal cancer screening, and a team approach to women's survivorship. We conducted document reviews and held semi-structured interviews with researchers, KUs, and other stakeholders within a cancer system organization. The analysis examined patterns across and within cases. RESULTS: Researchers and their respective knowledge users from 4 of the 5 cases agreed to participate. Eighteen individuals from 4 cases were interviewed. In 3 of 4 cases, there were mismatched expectations between researchers and KUs regarding KU role; participants recommended that expectations be made explicit from the beginning of the collaboration. KUs perceived that frequent KU turnover may have affected both KU engagement and the uptake of study results within the organization. Researchers and KUs found that sharing research results was challenging because the organization lacked a framework for knowledge translation. Uptake of research findings appeared to be related to the researcher having an embedded role in the cancer system organization and/or close alignment of the study with organizational priorities. Document reviews found evidence of planned IKT strategies in 3 of 4 cases; however, actual KU role/engagement on research teams was variable. CONCLUSIONS: Barriers to KU co-production of cancer health services research include mismatched expectations of KU role and frequent KU turnover. When a research study directly aligns with organizational priorities, it appears more likely that results will be considered in programming. Research teams that take an IKT approach should consider specific strategies to address barriers to KU engagement.
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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 | 0.077 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".