Proceedings of the 14th Annual Conference on the Science of Dissemination and Implementation in Health
Bibliographic record
Abstract
variety of dissemination and implementation research funded by our conference sponsors as well as other agencies, organizations, and systems.The additional 427 abstracts from the poster sessions are not included here but can be viewed at https://biomedcentral.spi- global.com/authorproofs/bmcproofs/index.php?id=yYzPofvuAO05132 022122711lqtKrxZnjn.Through the virtual platform, attendees were able to engage in conversations through the chat function during the plenary and concurrent sessions, allowing for participants to drive the interaction with presenters and create a valuable repository of relevant references and web-based resources which both speakers and participants shared during each of the sessions.The conference also featured virtual yoga, a social musical gathering, and daily morning coffee chats with D&I experts facilitating open discussions about key priorities for the field.These networking sessions again were hugely popular and well attended, providing attendees with the opportunity to connect with the leaders in the field.Another tremendous value of the virtual conference was the ability for us to host a significant number of participants from LMICs.After two years of virtual conferences, we look forward to welcoming attendees back to Washington, DC, for the next D&I Science conference this December.
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 | 0.165 | 0.253 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.096 | 0.025 |
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".