Proceedings from the 13th Annual Conference on the Science of Dissemination and Implementation
Bibliographic record
Abstract
In the midst of a global pandemic and heightened national attention to the pervasiveness and impact of systemic racism, the 13 th Annual Conference on the Science of Dissemination and Implementation in Health focused on the theme of "Dissemination and Implementation Science in a Dynamic, Diverse, and Interconnected World: Meeting the Urgent Challenges of our Time."Cohosted by the National Institutes of Health and AcademyHealth in collaboration with our co-sponsors the Agency for Healthcare Research and Quality (AHRQ), the Patient Centered Outcomes Research Institute (PCORI), the Robert Wood Johnson Foundation (RWJF), and the US Department of Veterans Affairs (VA), the conference was held virtually December 15-17, 2020.While many virtual events attract fewer attendees than their in-person counterparts, the 2020 conference had the highest number of registrants: 1,587.As in prior years, a majority of attendees work in academic settings while 100 were students, 28 were patient scholarship recipients, and 133 participants joined us from 29 low-and middle-income countries.Over the three-day agenda, we hosted keynote and plenary sessions, concurrent podium and poster sessions, workshops and discussion forums, and multiple networking events.The call for abstracts generated 767 submissions, including individual paper presentations, individual posters, and panel presentations spread across nine thematic tracks.Over two hundred reviewers from multiple disciplines, sectors, settings, and career stages comprehensively assessed the abstracts within each track, coordinated by the track leads.New this year, we piloted a streamlined process for developing concurrent sessions for multiple tracks: We increased the number of reviewers per abstract for more robust scores, which were then used by track leads to
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 | 0.089 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.101 | 0.050 |
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".