Proceedings from the 9th annual conference on the science of dissemination and implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ther unnecessary, of low value or wasteful (Institute of Medicine, 2013).The third plenary panel brought different perspectives on the enduring and evolving challenges in the dissemination of evidence and evidence-based practices as well as the opportunities emerging from innovations in the digital health sector.The plenary sessions were complemented by facilitated lunchtime discussions on these topics, as well as additional research priorities, which enabled more in-depth discussions, additional question and answer time, and brainstorming of future directions.Synopses of the lunchtime discussions are included in this supplement.The concurrent sessions were once again organized by tracks.Last year's tracks-Behavioral Health, Big Data and Technology for Dissemination and Implementation Research, Clinical Care Settings, Global Dissemination and Implementation, Promoting Health Equity and Eliminating Disparities, Health Policy Dissemination and Implementation, Prevention and Public Health, and Models, Measures and Methods-were maintained, and a new track on Precision Medicine was added, built upon the significant interest that emerged from last year's plenary and subsequent discussions at NIH, National Academy of Medicine, and beyond.The tracks again enabled conference participants to follow a consistent theme across the multiple sessions of the conference and to better group thematically the individual papers and posters submitted by the conference participants.This supplement also is organized by these track themes.The call for abstracts, including individual paper presentations, individual posters and panel presentations, resulted in 601 submissions, spread across the nine thematic tracks.Over one hundred reviewers from multiple disciplines, sectors, settings and career stage devoted their time to ensuring a comprehensive and expert review, and reviews were conducted within each track and coordinated by the track leads.For the final program, 19 oral abstract sessions, 9 panels, and 334 posters were presented over the two-day meeting, in addition to a "poster slam".Slides for the oral presentations and panels (with the agreement of the authors) were posted on the conference website (https://academy-health.confex.com/academyhealth/2016di/meetingapp.cgi/Home/0) and all abstracts were included on the conference webapp (https:// academyhealth.confex.com/academyhealth/2016di/meetingapp.cgi).New this year
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it