Precision Public Health Initiatives in Cancer: Proceedings from the Transdisciplinary Conference for Future Leaders in Precision Public Health
Bibliographic record
Abstract
BACKGROUND: Precision public health is an emergent field that requires transdisciplinary collaborations and leverages innovative approaches to improve population health. These opportunities have inspired a new generation of precision public health researchers. Despite burgeoning interest in precision public health, there are limited opportunities for researchers to convene and continue the momentum of this field. METHODS: The Transdisciplinary Conference for Future Leaders in Precision Public Health was the among the first events to bring together international researchers and practitioners to learn, network, and agenda set for the future of the field. The conference took place virtually on October 14 and 15, 2021. RESULTS: The conference spanned two days and featured a keynote address, speakers from public health disciplines who are international leaders in precision-based research, networking opportunities, a poster session, and research agenda setting activities. CONCLUSION: The conference was a critical first step to creating a shared international conversation about precision public health, especially among early-stage investigators. This allowed attendees to continue building their individual skills and international collaborations to support the growth of the field of precision public 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 | 0.042 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 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".