Tools, Strategies and Approaches to Anti-racism in Patient/Public Partnership in Research: a Scoping Review
Bibliographic record
Abstract
Support for patient/public involvement in health research has built considerable momentum over the last two decades, with funding agencies recommending patient/public-researcher partnership as a means to improve quality and relevance of research. Underpinning patient engagement in health research is the motto of “Nothing about us, without us”. Canada is culturally diverse, but patients who are actively involved in research are often from similar backgrounds, including race and ethnicity. Consequently, research ideas and products sometimes fall short of addressing the diverse needs of patients and members of the public. The lack of diversity also hinders patient-researcher partnerships from reaching their full potential to advance equitable patient-oriented research. This disparity was amplified during the early stage of the COVID-19 pandemic when decisions about research priorities were made by federal and provincial funders with little opportunity to consider the patient/public’s voices. This was concerning as groups that were the most affected by the pandemic, including racialized groups, were seldom involved when these decisions were made. At the core of patient engagement is diversity and inclusion. To this end, the Ontario SPOR SUPPORT Unit (OSSU) Patient Partner Working Group has identified a need to improve diversity, inclusion and equity in OSSU, other SPOR entities, and the broader world of patient engagement. It is recognized that existing challenges for patient partners navigating health research settings (e.g., research projects, networks, funding agencies, grant review panels) can be further amplified for individuals identifying as visible minority. Hence, a better understanding around approaches aimed at addressing equity in patient engagement is not only timely, but necessary. Therefore, the objective of this review is to synthesize the evidence on approaches, strategies and tools that promote anti-racism in patient/public-researcher partnership.
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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 | 0.186 | 0.350 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.058 | 0.039 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".