A scoping review to identify and describe the characteristics of theories, models and frameworks of health research partnerships
Bibliographic record
Abstract
Abstract Background Engaging users of health research, namely knowledge users, as partners in the research process may to lead to evidence that is more relevant to the users. This may optimize the uptake of evidence in healthcare practice, resulting in improved health outcomes or more efficient healthcare systems. However, barriers to involving knowledge users in the research process exist. Theories, models and frameworks may help guide the process of involving knowledge users and address barriers to engaging with knowledge users in research; however, there is little evidence identifying or describing the theories, models and frameworks of health research partnerships. Objectives Identify and describe theories, models and frameworks of health research partnerships. Report on concepts of knowledge user engagement represented in identified theories, models and frameworks. Methods We conducted a scoping review. Database (MEDLINE, Embase, CINAHL, PCORI) and ancestry and snowball searches were utilized. Included articles were written in English, published between January 2005 and June 2021, specific to health, a research partnership, and referred to a theory, model or framework. No critical appraisal was conducted. We developed a coding framework to extract details related to the publication (e.g. country, year) and theory, model or framework (e.g. intended users, theoretical underpinning, methodology, methods of development, purpose, concepts of knowledge user engagement). One reviewer conducted data extraction. Descriptive statistics and narrative synthesis were utilized to report the results. Results We identified 21 874 articles in screening. Thirty-nine models or frameworks were included in data analysis, but no theory. Two models or frameworks (5%) were underpinned by theory. Literature review was the method (n = 11, 28%) most frequently used to develop a model or framework. Guiding or managing a partnership was the most frequently reported purpose of the model/framework (n = 14, 36%). The most represented concept of knowledge user engagement was principles/values (n = 36, 92%). Conclusions The models and frameworks identified could be utilized by researchers and knowledge users to inform aspects of a health research partnership, such as guidance or implementation of a partnership. Future research evaluating the quality and applicability of the models and frameworks is necessary to help partners decide which model or framework to implement.
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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 | 0.084 | 0.272 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.081 | 0.084 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".