Training Highly Qualified Health Research Personnel: The Pain in Child Health Consortium
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Pain in Child Health (PICH) is a transdisciplinary, international research training consortium. PICH has been funded since 2002 as a Strategic Training Initiative in Health Research of the Canadian Institutes of Health Research, with contributions from other funding partners and the founding participation of five Canadian universities. The goal of PICH has been to create a community of scholars in pediatric pain to improve child health outcomes. METHODS: Quantitative analyses enumerated PICH faculty, trainees, training activities and scientific outputs. Interviews with PICH stakeholders were analyzed using qualitative methods capturing perceptions of the program's strengths, limitations, and opportunities for development and sustainability. RESULTS: PICH has supported 218 trainee members from 2002 through 2013, from 14 countries and more than 16 disciplines. The faculty at the end of 2013 comprised nine co-principal investigators, 14 Canadian coinvestigators, and 28 Canadian and international collaborators. Trainee members published 697 peer-reviewed journal articles on pediatric pain through 2013, among other research dissemination activities including conference presentations and webinars. Networks have been established between new and established researchers across Canada and in 13 other countries. Perceptions from stakeholders commended PICH for its positive impact on the development of pediatric pain researchers. Stakeholders emphasized skills and abilities gained through PICH, the perceived impact of PICH training on this research field, and considerations for future training in developing researchers in pediatric pain. CONCLUSIONS: PICH has been successfully developing highly qualified health research personnel within a Canadian and international community of pediatric pain scholarship.
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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 | 0.058 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".