Bridging the divide: supporting and mentoring trainees to conceptualize, plan, and integrate engagement of people with lived experience in health research
Bibliographic record
Abstract
Health researchers are encouraged by governments, funders, and journals to conduct research in partnership with people with lived experience. However, conducting research with authentic engagement and partnership with those who are experts by experience, but may not have research methods training, requires resources and specialized skills. The McMaster Collaborative for Health and Aging developed a fellowship program for trainees that builds their capacity to conduct research in partnership with older adults with relevant lived experience. We share this case example, with its successes and challenges, to encourage creative reformation of traditional research training. The Collaborative used an iterative design process, involving researchers, trainees and older adult and caregiver partners, who, together, developed a fellowship program for trainees that provides support and mentorship to plan and conduct health research in partnership with people with lived experience. Since 2022, the Partnership in Research Fellowship has been offered biannually. The application process was purposefully designed to be both constructive and supportive. Opportunities for one-on-one consultations; key resources, including a guide for developing a plan to involve people with relevant lived experience; and feedback from older adult and researcher reviewers are provided to all applicants. Successful trainees engage with older adult and caregiver partners from the Collaborative to advance and enhance a range of skills from facilitating partner meetings to forming advisory committees. Trainees are awarded $1500 CAD to foster reciprocal partnerships. Ten graduate students from various disciplines have participated. Trainees reported positive impacts on their knowledge, comfort, and approach to partnered research. However, the time required for undertaking partnered research activities and involving diverse partners remain obstacles to meaningful engagement. Partnering with people with lived experience in the design of educational programs embeds the principles of partnership and can increase the value and reward for all involved. We share the Partnership in Research Fellowship as a case example to inspire new and transformative approaches in research training and mentorship that will move the field forward from engagement theory to meaningful enactment. Health researchers are encouraged by governments, funders, and journals to conduct research in partnership with individuals with relevant health conditions or experience. However, conducting research with individuals who are experts by experience, but may not have research training, requires resources and specialized skills. The McMaster Collaborative for Health and Aging developed a fellowship program to support and mentor trainees to conduct their research in partnership with people with lived experience and turn engagement theory into action. The Collaborative involved researchers, trainees, and older adults in the development of the fellowship program. Since 2022, the Partnership in Research Fellowship has been offered twice a year. The application process was designed to be both supportive and informative. Opportunities for one-on-one consultations; key resources, including guiding questions to consider when planning to involve people with relevant lived experience; and feedback from older adults and researchers, are provided to all applicants. Each trainee receives $1500 CAD to support building strong, two-way partnerships. Since the fellowship’s launch, 10 graduate students from different fields have participated. Trainees reported improvements in their knowledge and comfort to partner with people with lived experience in research. However, challenges, such as the extra time needed for conducting partnered research as well as locating and involving those from diverse backgrounds, were identified. Involving people with lived experience in the design of research training incorporates partnership principles and may enhance the benefits and satisfaction for everyone involved. We share the Partnership in Research Fellowship, as an example, to inspire new approaches in research training and mentorship.
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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 | 0.097 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.008 | 0.057 |
| Research integrity | 0.009 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 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".