Partnered Recruitment: Engaging Individuals With Lived Experience in the Recruitment of Co‐Design Participants
Bibliographic record
Abstract
BACKGROUND: Young adults with type 1 diabetes (T1D) face complex health challenges, including a heightened risk for distress. To counter this distress, there is a need to develop accessible, acceptable comprehensive care solutions that integrate diabetes and mental health care to enhance self-efficacy and counter mental health challenges in this population. OBJECTIVE: To describe the engagement of individuals with lived experience of T1D and mental health challenges in the development of a recruitment strategy to support the co-design of an innovative integrated care programme. RESULTS: Seven individuals with lived experience formed a Partner Advisory Council (PAC) to recruit young adults (18-29 years old) living with T1D, their friends or family and health researchers and professionals in co-design interviews (n = 19) and co-design events (n = 12). The PAC played a key role in developing a comprehensive recruitment strategy, overcoming traditional barriers and stigmas in the design of an integrated model of care. CONCLUSION: Assuming the presence of mental health challenges in young adults living with T1D during recruitment had far-reaching impacts on the development of a whole-person and integrated diabetes and mental health care solution. The efficient recruitment of this sample provided invaluable insights into the nuanced challenges experienced by young adults with T1D, the individual skills developed in response to their mental health challenges and the ways that this understanding can shape future programming to support mental health, quality of life and well-being. The ongoing involvement of the PAC as co-researchers underscores the enduring impact of patient engagement in developing integrated care solutions. PATIENT OR PUBLIC CONTRIBUTION: The co-design of the TECC-T1D3 model was enriched by the invaluable contributions of individuals with lived experience. This included the engagement of a diverse PAC in the recruitment of participants in co-design interviews and co-design events. PAC members actively participated in research decision-making with their insights informing a robust recruitment strategy. Beyond recruitment, PAC members continue to serve as co-researchers, shaping ongoing research and actively contributing to the TECC-T1D3 project. Six PAC members are co-authors on this manuscript.
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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 | 0.090 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".