Challenges of diversity and inclusion in defence emergency management and preparedness during the COVID-19 pandemic
Bibliographic record
Abstract
LAY SUMMARY The Canadian military’s recent mission in support of long-term-care homes in Ontario, and the alleged abuses reported, demonstrates the urgent need to address challenges associated with diversity and inclusion in defence emergency management and preparedness during the COVID-19 pandemic. This article reviews the social and health impacts of the COVID-19 pandemic on diverse groups within the Canadian Defence Team and across Canada, with a particular focus on visible minorities, Indigenous people, women, older adults, persons with disabilities, and lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit communities. The review indicates that the pandemic widened the existing physical and mental health disparities and socio-economic inequities affecting these groups. To address these challenges, and to better understand the needs of diverse groups in the pandemic context, several recommendations for the Defence Team are proposed to incorporate into daily encounters with diverse groups and communities affected by COVID-19. The recommendations are designed to enable the Defence Team to establish positive and sustainable relations with diverse communities and to increase community resilience and defence emergency operational readiness.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".