Collaboration Between Researchers and Knowledge Users in Health Technology Assessment: A Qualitative Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Collaboration between researchers and knowledge users is increasingly promoted because it could enhance more evidence-based decision-making and practice. These complex relationships differ in form, in the particular goals they are trying to achieve, and in whom they bring together. Although much is understood about why partnerships form, relatively little is known about how collaboration works: how the collaborative process is shaped through the partners' interactions, especially in the field of health technology assessment (HTA)? This study aims at addressing this gap in the literature in the specific context of HTA. METHODS: We used a qualitative descriptive design for this exploratory study. Semi-structured interviews with three researchers and two decision-makers were conducted on the practices related to the collaboration. We also performed document analysis, observation of five team meetings, and informal discussion with the participants. We thematically analyzed data using the structuration theory and a collective impact framework. RESULTS: This study showed that three main contextual factors helped shape the collaboration between researchers and knowledge users: the use of concepts related to each field; the use of related expertise; and a lack of clearly defined roles in the project. Previous experiences with the topic of the research project and a partnership based on "a give and take" relationship emerged as factors of success of this collaboration. CONCLUSION: By shedding light on the structuration of the collaboration between researchers and knowledge users, our findings open the door to a poorly documented field in the area of HTA, and additional studies that build on these early observations are welcome.
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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 | 0.060 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| 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".