Investing in Leadership Development: A Tool for Systems Change in the Community Health Center Field
Bibliographic record
Abstract
Over the course of 12 years, the Blue Shield of California Foundation committed nearly $20 million to growing a pool of community health center leaders who were prepared to be effective agents of change in their organizations and in the safety net field. This signature investment, the Clinic Leadership Institute, was implemented in partnership with the Healthforce Center at University of California, San Francisco, in anticipation of a generation of California health center leaders beginning to transition into retirement. During the institute's 10 cohorts, access to community health centers dramatically increased with the Affordable Care Act, and this — coupled with rising costs of health care — continued to underscore how crucial community health centers were to accessible and quality care for poor and underserved populations. A study spanning 10 cohorts of alumni found that the institute served a critical role in supporting community health center leaders and their organizations in navigating these changes, while also building alumni networks advocating for community health centers in county- and state-level policy. The program equipped 258 individuals to lead and deliver care in a field marked by continuous change, complexity, and mounting demand. Drawing on these findings, we make the case that investment in leadership development is a critical philanthropic tool for field building and, ultimately, systems change. We explore how the foundation made the most of this investment through intentional funding, design, and strategic considerations.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".