Intersecting challenges and ways forward: The impact of the COVID-19 pandemic on an urban First Nations community in Southern Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic has had wide-ranging impacts on communities worldwide, with Indigenous communities in southern Ontario, Canada, being no exception. Partnering with Aamjiwnaang First Nation, we explored the multifaceted impacts of the pandemic and learnings for the future. This study utilized semi-structured interviews with the community's pandemic committee and other front line essential services (N = 12) to explore the nuanced dimensions of the pandemic's effects. Data were analysed using a template approach to codebook thematic analysis to examine various aspects of the pandemic response. Five main themes were identified, including: (i) Wellbeing and mental health, (ii) Work-life balance, (iii) Community and social factors, (iv) Organizational dynamics, and (v) Lessons learned and future planning. Our findings unveiled a multifaceted spectrum of challenges, encompassing socioeconomic, psychological, and organizational aspects, which the First Nations community encountered amidst the pandemic. Despite these challenges, the commitment to community adaptation and collaboration highlighted the resilience cultivated through strong Indigenous leadership, trusting partnerships, and transparent communication, contributing to an effective response. This research stresses the need for future pandemic preparedness efforts to prioritize Indigenous leadership and address the social and cultural determinants of Indigenous health. Additionally, to effectively address future environmental and health emergencies, there is a pressing need to adopt an all-hazards approach and develop comprehensive, yet adaptable plans tailored to meet the diverse needs of communities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 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".