Creating a Culture of Equity: Building Awareness Within the Montgomery County Department of Health and Human Services
Bibliographic record
Abstract
The Robert Wood Johnson Foundation recognizes Montgomery County as the county with the best health outcomes, length of life, and quality of life in all of Maryland. While impressive, the overall statistics do not reflect the disparities among certain population groups in the county. As a major provider of safety net health and social services to county residents, the Montgomery County Department of Health and Human Services (MCDHHS) wants to empower staff to think and act differently to generate better outcomes for disadvantaged communities. Among the many phases toward building an equity value driven organization, the Department felt that cultivating a common understanding and buy-in from all levels of staff is critical to a cultural shift. In 2014, a comprehensive workshop was implemented to raise awareness and encourage courageous conversations. The workshop seeks to create a common understanding of equity, its principles and applied strategies, and provide Department staff with the tools to treat their colleagues, customers, and clients more equitably. To date, 828 staff, contractors, and community partners have completed the workshop and 47 staff members have trained as workshop peer facilitators. This paper will explore in more detail the information-related factors and processes present in the workshop and their equity impact on the Department’s practices.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".