Illuminating and mitigating the evolving impacts of COVID-19 on ethnocultural communities: a participatory action mixed-methods study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has exacerbated disparities in poverty and illness for people in vulnerable circumstances in ethnocultural communities. We sought to understand the evolving impacts of COVID-19 on ethnocultural communities to inform intersectoral advocacy and community action. METHODS: The Illuminate Project used participatory action research, with cultural health brokers as peer researchers, from Sept. 21 to Dec. 31, 2020, in Edmonton, Alberta. Twenty-one peer researchers collected narratives from members of ethnocultural communities and self-interpreted them as they entered the narratives into the SenseMaker platform, a mixed-method data collection tool. The entire research team analyzed real-time, aggregate, quantitative and qualitative data to identify emerging thematic domains, then visualized these domains with social network analysis. RESULTS: Brokers serving diverse communities collected 773 narratives. Identified domains illuminate the evolving and entangled impacts of COVID-19 including the following: COVID-19 prevention and management; care of acute, chronic and serious illnesses other than COVID-19; maternal care; mental health and triggers of past trauma; financial insecurity; impact on children and youth and seniors; and legal concerns. We identified that community social capital and cultural brokering are key assets that facilitate access to formal health and social system supports. INTERPRETATION: The Illuminate Project has illustrated the entangled, systemic issues that result in poor health among vulnerable members of ethnocultural communities, and the exacerbating effects of COVID-19, which also increased barriers to mitigation. Cultural brokering and community social capital are key supports for people during the COVID-19 pandemic. These findings can inform policy to reduce harm and support community resiliency.
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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 | 0.087 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".