The Pediatric Heart Network Scholar Award programme: a unique mentored award embedded within a multicentre network
Bibliographic record
Abstract
BACKGROUND: The Pediatric Heart Network designed a career development award to train the next generation of clinician scientists in paediatric-cardiology-related research, a historically underfunded area. We sought to identify the strengths/weaknesses of the programme and describe the scholars' academic achievements and the network's return on investment. METHODS: Survey questions designed to evaluate the programme were sent to applicants - 13 funded and 19 unfunded applicants - and 20 mentors and/or principal investigators. Response distributions were calculated. χ2 tests of association assessed differences in ratings of the application/selection processes among funded scholars, unfunded applicants, and mentors/principal investigators. Scholars reported post-funding academic achievements. RESULTS: Survey response rates were 88% for applicants and 100% for mentor/principal investigators. Clarity and fairness of the review were rated as "clear/fair" or "very clear/very fair" by 98% of respondents, but the responses varied among funded scholars, unfunded applicants, and mentors/principal investigators (clarity χ2=10.85, p=0.03; fairness χ2=16.97, p=0.002). Nearly half of the unfunded applicants rated feedback as "not useful" (47%). "Expanding their collaborative network" and "increasing publication potential" were the highest-rated benefits for scholars. Mentors/principal investigators found the programme "very" valuable for the scholars (100%) and the network (75%). The 13 scholars were first/senior authors for 97 abstracts and 109 manuscripts, served on 22 Pediatric Heart Network committees, and were awarded $9,673,660 in subsequent extramural funding for a return of ~$10 for every scholar dollar spent. CONCLUSIONS: Overall, patient satisfaction with the Scholar Award was high and scholars met many academic markers of success. Despite this, programme challenges were identified and improvement strategies were developed.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".