A Gendered Emergency Framework: Integrating Sex, Gender and Equity into Emergency Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As disasters, climate emergencies, public health crises, and security threats and conflicts increase in Canada, so do concerns about their inequitable impacts. Canada’s 2023 Chief Public Health Officer’s Report, Creating the Conditions for Resilient Communities: A Public Health Approach to Emergencies, highlighted the unequal impacts of emergencies in Canada and advocated for an improved public health and health promotion response. This article describes the Gendered Emergency Management Framework (GEM-F), developed as a practical tool to support emergency personnel, planners, and policy makers in integrating sex, gender, trauma and equity-informed considerations across the emergency management continuum, applicable to climate disasters, pandemics or conflict situations. The GEM-F is built on academic evidence, grey literature, and consultations with Canadian and Australian experts, and suggests the integration of sex and gender based analysis plus (SGBA+), and trauma-informed, equity-oriented, and gender transformative approaches into all phases of emergency management. The consistent application of the GEM-F in policy, practice and training could improve preparedness and post-event outcomes, along with overall gender and health equity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it