Recommended Mitigation Measures for an Influenza Pandemic in Remote and Isolated First Nations Communities of Ontario, Canada: A Community-Based Participatory Research Approach
Bibliographic record
Abstract
Influenza pandemics disproportionately impact remote and/or isolated Indigenous communities worldwide. The differential risk experienced by such communities warrants the recommendation of specific mitigation measures. Interviewer-administered questionnaires were conducted with adult key health care informants from three remote and isolated Canadian First Nations communities of sub-Arctic Ontario. Forty-eight mitigation measures (including the setting, pandemic period, trigger, and duration) were questioned. Participants’ responses were summarized and collected data were deductively and inductively coded. The participants recommended 41 of the questioned mitigation measures, and often differed from previous literature and national recommendations. Results revealed that barriers, such as overcrowded housing, limited supplies, and health care infrastructure, impacted the feasibility of implementing mitigation measures. These findings suggest that pandemic plans should recommend control strategies for remote and isolated Canadian First Nations communities that may not be supported in other communities. These findings highlight the importance of engaging locally impacted populations using participatory approaches in policy decision-making processes. Other countries with remote and/or isolated Indigenous communities are encouraged to include recommendations for mitigation measures that specifically address the unique needs of such communities in an effort to improve their health outcomes during the next influenza pandemic.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".