Celebrating a Quarter-Century of Public Domain Research: 25th Annual Conference of the Health Care Systems Research Network
Bibliographic record
Abstract
The 25th annual conference of the Health Care Systems Research Network (HCSRN) was held April 10-12 in Portland, Oregon, attracting 420 attendees. The HCSRN, a consortium of 18 community-based research organizations embedded in or affiliated with large health care delivery systems, has hosted annual research conferences since 1994. The primary objective of the conference is to convene researchers, project staff, funders, and other stakeholders to share latest scientific findings and cultivate new partnerships among research teams, patients, and clinicians. Collaboration is the cornerstone of the HCSRN's success; the conference serves as a catalyst for a variety of collaborative ventures as well as tactics and approaches to more effective and efficient research. This year's program included 70 distinct scientific presentations, plus nearly 100 posters, and spanned diverse content offerings that mirrored the diversity of the HCSRN and its collaborators. Plenary sessions imparted insights on ways that data science and approaches to collaborative design in health care can speed the translation of research into practice.
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 | 0.074 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".