Family Medicine–Specific Practice-Based Research Network Productivity and Clinical and Translational Sciences Award Program Affiliation
Bibliographic record
Abstract
OBJECTIVE: Practice-based research networks (PBRNs) are groups of practices that work together to conduct research. Little is known about the degree to which PBRNs may be achieving success. This is the first general survey of family medicine-based PBRN directors in the United States and Canada to examine research productivity outcomes of PBRNs and explore the association between Clinical and Translational Science Awards (CTSA) program affiliation and PBRN outcomes. METHODS: The Council of Academic Family Medicine Educational Research Alliance conducted the survey and e-mailed it to 102 PBRN directors from the Agency for Healthcare Research and Quality's registration. RESULTS: A total of 54 (56%) PBRN directors responded to the survey. PBRNs with an affiliation with a CTSA program were more likely to report completion of quality improvement research and participation in multiple PBRN collaboration research projects. PBRNs affiliated with CTSA programs were less likely to report maintaining funding as a significant barrier. CONCLUSIONS: CTSA involvement with PBRNs results in family physician scientists' completing research and disseminating this research through publication. Also, PBRNs with CTSA partnerships have more funding availability. PBRN partnership with a CTSA is beneficial in furthering research in family medicine.
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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".