PARTNERSHIPS TO IMPROVE AGING IN CALIFORNIA: PERSPECTIVES FROM HRSA’S GERIATRIC WORKFORCE ENHANCEMENT PROGRAMS
Bibliographic record
Abstract
Background: To improve models of care for older adults, HRSA’s Geriatrics Workforce Enhancement Program (GWEP) encouraged academic institutions to partner with health and/or social service providers. In 2016, evaluators from five California GWEPs began to study their partnerships and to determine characteristics of success. Methods: Evaluators developed a mixed-method approach. Partners (n=38) completed a survey that measured five attributes (governance, administration, autonomy, mutuality, and norms/trust) with a 17-item instrument, and perceived success with an 8-item instrument. To determine context, experiences and results, evaluators conducted semi-structured, 15-question interviews that were audio recorded and transcribed for thematic analysis. Through iterative coding by consensus, a code structure emerged and was applied to all interviews. Results: Partners reported high levels of success (mean=8.2, 10-point scale), and scores for attributes ranged from 5.9 (7-point scale) for administration to 6.4 for autonomy with mutuality (odds ratio of 8.1, p=0.002) and norms/trust (odds ratio=19.0, p=0.019) most highly correlated with success. Interviews with 20 organizations captured a range of partnership structures, from coordination to collaboration, and experiences of dissatisfaction were associated with a failure to acknowledge differences in applied goals. Discussion: To meet the goals of HRSA’s GWEP initiative to improve care for older adults, successful cross-sector partnerships are critical. A mixed-method study of five diverse GWEPs in California showed that partnership aspects of mutuality (e.g., all partners benefit from the collaboration) and norms/trust (e.g., organizations can count on each other) appeared to contribute the most to perceived success. Additionally, prioritizing understanding of applied goals can decrease dissatisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".