Peer-to-Peer Patient Partner Recruitment: Enhancing Peer Engagement for Successful Patient Partner Recruitment in a National Kidney Health Research Network
Bibliographic record
Abstract
Background: Patient partnership in kidney health research is recognized as an equitable approach to improving patient outcomes. Patient Partners/People with Lived Experience (PWLE) are co-researchers with lived experience of illness as patients or caregivers. Methods to recruit PWLE are poorly understood and not widely reported on. Together with two PWLE as co-researchers, we present successful PWLE recruitment strategies within the national Can-SOLVE CKD research network, funded in part by the Canadian Institutes of Health Research’s Strategy for Patient-Oriented Research whose focus is on transforming kidney research through meaningful patient engagement. We present the results of the co-development of practical PWLE recruitment methods and tools, including a peer-to-peer recruitment strategy. Methods: We conducted an environmental scan of existing research in academic publications; gathered data from the network’s nine project reports and reviews, from the network website that connects researchers with PWLE; and, finally we conducted a survey and roundtable discussions with two network patient advisory councils to identify the most effective recruitment methods and strategies. Data were consolidated and analyzed thematically to identify successes and challenges to inform the development of a PWLE recruitment toolkit. Results: A common challenge among researchers is the recruitment of PWLE; especially for recruiting ethnically- and gender-diverse participants. Effective PWLE recruitment strategies are categorized thematically along socio-spatial distances, including: (1) macro-level; (2) meso-level; and (3) micro-level and temporally according to illness/treatment stabilization. Peer engagement in the form of peer-to-peer recruitment is the most common route to patient partnership; however, a multi-faceted approach is the most effective way to reach the most diverse participants. Conclusion: Recruiting PWLE as partners in research in the kidney health research context may present some challenges, especially for harder-to-reach participants; however, by targeting various levels of reach, considering timing of recruitment and promoting peer-to-peer recruitment will enable a strategy for success. Funding: Private Foundation Support, Government Support – Non-U.S.
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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 | 0.121 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".