Proceedings from the 2023 transdisciplinary conference for future leaders in precision public health “Applying Implementation Science to Precision Public Health”
Bibliographic record
Abstract
Precision public health (PPH) approaches use big data to inform tailored, population-level interventions. The field has roots in genomics, but it has expanded to encompass data-informed public health programs across various types of data or applications. The Precision Public Health Network hosted a 2023 conference focused on implementation science-the study of how to integrate PPH programs into practice. Some implementation needs that emerged across speakers included establishing robust evidence of clinical utility and feasibility, disseminating clinical best practices through guidelines and tools for providers, sharing tools or information to reduce duplicated efforts across settings, and considering context-specific factors. Considering feasibility, setting-specific factors, and meaningful engagement with relevant user groups throughout the research and implementation process are critical to the successful and sustainable implementation of PPH programs. The Network also hosted an interactive workshop to generate ideas and ongoing collaboration on essential outcomes or data measures for PPH programs, and strategies to center health equity in PPH. This conference and workshop are part of the ongoing work of the PPHN to convene experts across disciplines and settings, share knowledge, and galvanize the field of PPH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.060 | 0.019 |
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".