Saying it out loud: explicit equity prompts for public health organization resilience
Bibliographic record
Abstract
Introduction: In the early days of the COVID-19 pandemic there were numerous stories of health equity work being put "on hold" as public health staff were deployed to the many urgent tasks of responding to the emergency. Losing track of health equity work is not new and relates in part to the need to transfer tacit knowledge to explicit articulation of an organization's commitment to health equity, by encoding the commitment and making it visible and sustainable in policy documents, protocols and processes. Methods: We adopted a Theory of Change framework to develop training for public health personnel to articulate where and how health equity is or can be embedded in their emergency preparedness processes and documents. Results: Over four sessions, participants reviewed how well their understanding of disadvantaged populations were represented in emergency preparedness, response and mitigation protocols. Using equity prompts, participants developed a heat map depicting where more work was needed to explicitly involve community partners in a sustained manner. Participants were challenged at times by questions of scope and authority, but it became clear that the explicit health equity prompts facilitated conversations that moved beyond the idea of health equity to something that could be codified and later measured. Over four sessions, participants reviewed how well their understanding of disadvantaged populations were represented in emergency preparedness, response and mitigation protocols. Using equity prompts, participants developed a heat map depicting where more work was needed to explicitly involve community partners in a sustained manner. Participants were challenged at times by questions of scope and authority, but it became clear that the explicit health equity prompts facilitated conversations that moved beyond the idea of health equity to something that could be codified and later measured. Discussion: Using the indicators and prompts enabled the leadership and staff to articulate what they do and do not know about their community partners, including how to sustain their involvement, and where there was need for action. Saying out loud where there is - and is not - sustained commitment to achieving health equity can help public health organizations move from theory to true preparedness and resilience.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".