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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.100 |
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; both teacher heads agree on what is shown here.
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".