‘There has to be some chemistry there’: an interpretive description exploring the experiences, motivations and dynamics of partnered child health research
Bibliographic record
Abstract
Abstract Background Research partnerships between researchers and knowledge users are increasingly valued for their role in generating relevant and impactful research. However, the dynamics of these partnerships, including motivations, challenges, and relational aspects, remain underexplored. This study aimed to understand the experiences of researchers and knowledge users engaged in partnered child health research. Methods We used an interpretive description approach to conduct and analyze semi-structured interviews with researchers and knowledge users involved in Canadian partnered child health research projects. Data analysis was iterative, involving inductive coding, theme development, and reflection with an interprofessional colleague with lived experience of a congenital health condition. Results Fifteen individuals (12 researchers and 3 knowledge users) participated. We identified three key themes. (1) Diverse drivers: role-based motivations and perceived effects – Participants described diverse, role-based motivations for engaging in partnered research. Researchers were primarily driven to enhance research impact, while knowledge users emphasized advocacy and credibility. Individuals embodying dual roles with research expertise and professional experience focused on practical relevance and improving health outcomes. (2) It’s all about the relationships – Relational quality was central to successful partnerships. While power dynamics shifted towards greater equity over time, achieving fully equitable relationships remained complex. (3) Navigating evolutions and tensions – Academic structures continue to create tensions despite a growing cultural shift toward embracing partnership approaches. Key challenges included insufficient recognition for partnership efforts, and funding mechanisms often misaligning with partnership principles. Conclusions This study underscores the importance of understanding role-specific motivations, prioritizing relational quality, and addressing systemic challenges in child health research partnerships. Findings provide insights into key dynamics that help navigate both successes and challenges in these partnerships. Continued efforts are needed to address misalignments across systems and structures, support relationship building, and promote sustained collaboration.
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.032 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".