Exploring the “how” in research partnerships with young partners by experience: lessons learned in six projects from Canada, the Netherlands, and the United Kingdom
Bibliographic record
Abstract
BACKGROUND: Involvement of young partners by experience in research is on the rise and becoming expected practice. However, literature on how to promote equitable and meaningful involvement of young people is scarce. The purpose of this paper is to describe and reflect on different approaches between researchers and young partners by experience based on six research projects conducted in Canada, Netherlands, and United Kingdom. METHODS: From six exemplar research projects, at least one researcher and one young partner by experience were asked to collaboratively (1) describe the project; (2) summarise the values and practicalities of the project; and (3) reflect on their partnership. Thematic analysis was applied to the findings from these reflective exercises, which included meeting summaries, recordings, and notes. RESULTS: All projects shared similar values, including mutual respect between all team members. Young partners were offered a variety of opportunities and approaches to being involved, for example in recruiting participants, co-analysing or (co-)presenting results. Supports were provided to the teams in a variety of ways, including organizing accessible meetings and having dedicated facilitators. Regular and proactive communication was encouraged by using asynchronous modes of communication, establishing reference documents, and a personal approach by facilitators. Facilitators aimed to tailor the needs of all team members by continuously discussing their preferred roles in the project. While most projects did not offer formal research training, various learning and skill development opportunities were provided throughout, including presenting skills or advocacy training. CONCLUSION: With this paper, we demonstrated the value of reflection, and we invite others to reflect on their partnerships and share their lessons learned. Our recommendations for involvement of young people in research are: (1) Remember that it is okay to not know what the partnership might look like and there is no single recipe of how to partner; (2) Take the time to invest in partnerships; (3) Provide ongoing opportunities to reflect on partnerships; (4) Consider how to balance the power dynamics; and (5) Consider how to incorporate diversity in the background of young partners in research.
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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 | 0.055 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.024 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".