Developing a Trainee Advisory Committee Within a Pediatric Hospital Medicine Research Network
Bibliographic record
Abstract
Medical research networks are essential for advancing clinical care. Despite the recognized importance of building research capacity and training future pediatric researchers, trainee engagement within these research networks remains inconsistent. To address this, the Paediatric Inpatient Research Network (PIRN) established the Trainee Advisory Committee (TAC) in 2022 to foster trainee participation in pediatric hospital medicine research. This article describes the development, implementation, and early outcomes of the TAC, highlighting key successes and challenges. In its first 2 years, the TAC focused on increasing trainee engagement and facilitating cross-center, trainee-led research. A national survey on pediatric residency research experiences informed TAC priorities and led to a peer-reviewed publication. The TAC expanded in its second year, introducing structured subcommittees and defined leadership roles to enhance productivity and sustainability. These efforts increased membership, annual meeting attendance, and abstract submissions by trainees. Challenges included defining the TAC's scope, distributing roles, and managing turnover caused by short trainee tenures. Future goals include increasing trainee involvement in PIRN-led research and developing a mentorship program. The PIRN TAC provides a model for integrating trainees into research networks and strengthening the pipeline of future pediatric researchers. By sharing our experience, we offer a framework for other subspecialty research networks seeking to enhance trainee engagement.
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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 | 0.120 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".