Clinical and translational science award hub portfolio analysis and interorganizational collaborations with Los Angeles County to improve population health and health care delivery (ID 1565096)
Bibliographic record
Abstract
Demonstrating impact in the Clinical and Translational Science Award (CTSA) Program is crucial to continue governmental, taxpayer, institutional, and donor support and investment. We present an innovative portfolio analysis to summarize the Scientific Achievement Translational Science Impact at the hub level. Additionally, a unique feature of the UCLA CTSA hub includes the many interorganizational collaborations with Los Angeles County (LAC). This is the first study to examine the Translational Science Benefits Model (TSBM) impact on projects with CTSA hub-county interorganizational collaborations. A Framework for Evaluating Scientific Achievement Translational Science Impact (SATSI) was used to guide the analyses, with impact indicators derived from the TSBM: (i) clinical and medical, (ii) community and public health, (iii) economic, and (iv) policy and legislation. Two major data sources were used for the evaluation: (i) The CTSI’s Longitudinal Scientific Achievement and Impact survey (LSAS-I), and (ii) longitudinal interviews with principal investigators who reported high-impact projects in hub-county collaborations. We reported baseline data from 2 years of LSAS-I data showing n = 507 new CTSA-assisted grants and the associated demonstrated and potential impact using the hub portfolio analysis. Eighteen (n = 18) of these grants involved a hub-county interorganizational collaboration. Among these, we identified the highest impact projects and developed impact stories and vignettes describing improvements in health care delivery and population health. Our research offers a model for other CTSA hubs to summarize impact using the hub portfolio analysis, and to partner with local public health departments and governmental agencies to address health concerns in low-income and at-risk populations. This research directly addresses the mission of the UCLA hub, “to produce and implement innovations that impact the greatest health needs of Los Angeles and the nation.”
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".