Enhancing Health Equity in Emergencies: Implementing an Equity Officer in Public Health Emergency Responses
Bibliographic record
Abstract
Emergencies, particularly those with public health impacts, disproportionately affect priority populations, thereby exacerbating existing health disparities. To address these challenges, emergency management practitioners across various sectors must explore actionable ways to enhance health equity throughout the emergency management cycle. Following the COVID-19 pandemic, Ottawa Public Health conducted an environmental scan and literature review that revealed limited research or resources on how to fully incorporate equity into an emergency response structure. This paper examines local initiatives in Ottawa, Ontario during emergency responses, and the need for a formal role to support those most negatively impacted. These findings led to the development of an Equity Officer position, along with a role-specific checklist. The authors recommend the implementation of this unique role, thus ensuring a core member of the incident command team is dedicated to providing support to priority populations and recommend tailored response actions during an emergency.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".