Characterizing research partnerships in child health research: A scoping review
Bibliographic record
Abstract
Research partnerships between researchers and knowledge users (KUs) in child health are understudied. This study examined the scope of KU engagement reported in published child health research, inclusive of health research partnership approaches and KU groups. Search strategies were developed by a health research librarian. Studies had to be in English, published since 2007, and were not excluded based on design. A two-step, multiple-person hybrid screening approach was used for study inclusion. Data on study and engagement characteristics, barriers and facilitators, and effects were extracted by one reviewer, with 10% verified by a second reviewer. Three hundred fifteen articles were included, with 243 (77.1%) published between 2019 and 2021. Community-based participatory research was the most common approach used ( n = 122, 38.3%). Most studies ( n = 235, 74.6%) engaged multiple KU groups (range 1–11), with children/youth, healthcare professionals, and parents/families being most frequently engaged. Reporting of barriers and facilitators and effects were variable, reported in 170 (53.8%) and 197 (62.5%) studies, respectively. Publications have increased exponentially over time. There is ongoing need to optimize evaluation and reporting consistency to facilitate growth in the field. Additional studies are needed to further our understanding of research partnerships in child health.
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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 | 0.089 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.039 | 0.044 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".