Establishing a Health Equity Office: The Importance of Recentering Equity
Bibliographic record
Abstract
Objectives: The Pediatric Health Equity Collaborative (PHEC) set out to describe the best practices for establishing a health equity-focused office within a clinical setting. Study Design: Survey and in-depth interviews of the members of the PHEC comprised pediatric care delivery systems in the United States and Canada. Methods: Human-centered design methods were utilized in an iterative fashion to develop and agree on survey and interview domains. The final seven domains were as follows: (1) history of the office, (2) general description of the office, (3) position of the office in the organization, (4) budget and finance, (5) stakeholders, (6) community engagement, and (7) measuring outcomes. Interviews were analyzed using an applied thematic approach to inductively identify themes until saturation was achieved. Results: PHEC participants articulated several key implementation factors in the development of a health equity office. First, the history of the office is important and has the potential to determine the office's scope of work and sphere of influence. Second, a health equity office can provide crosscutting organizational direction, stability, and execution of equity efforts, reducing the effects of siloing. Third, high-level leadership buy-in provides time and financial resources. Finally, a health equity office should be centrally involved in the collection, analysis, and reporting of equity-focused metrics. Conclusions: A health equity-focused office can play an integral and sustaining role in representing and focusing equity efforts across an organization, measuring processes and outcomes, and helping to develop the equity mission and vision.
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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 | 0.135 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".