Proceedings of the Fifth Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2019: where the rubber meets the road: the intersection of research, policy, and practice - part 1
Bibliographic record
Abstract
The Society for Implementation Research Collaboration (SIRC) evolved as a society following a National Institute of Mental Health (NIMH)-funded conference grant (NIMH 1R13MH086159-01A1, PI Comtois) and has continued to develop as an international society [ 1 ]. At the 5th biennial conference held in in Seattle, WA, USA, on September 12–14, 2019, we announced that we had incorporated SIRC as an entity and obtained non-profit status with generous assistance from the Business Innovations Clinic at the University of Arkansas Little Rock, Bowen School of Law. SIRC’s goal is to improve the implementation of effective practices in behavioral health, health, and social care, notably through collaboration among communities, researchers, purveyors of evidence-based practices, practitioners, and policy makers. To include and support a variety of member types, SIRC created Networks of Expertise (NoE). These networks include the Student, New Investigator, Established Investigator, Practitioner, and Mechanisms NoE. Each NoE focuses on activities relevant to their members, such as pairing Students with New or New with Established Investigator mentors, hosting office hours to share expertise, and developing conference content relevant to their interests. Conference activities related to the NoEs are highlighted throughout this summary.
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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 | 0.182 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".