Assessing the effectiveness and feasibility of implementing mitigation measures for an influenza pandemic in remote and isolated First Nations communities: a qualitative community-based participatory research approach
Bibliographic record
Abstract
INTRODUCTION: The next influenza pandemic is predicted to disproportionately impact marginalized populations, such as those living in geographically remote Aboriginal communities, and there remains a paucity of scientific literature regarding effective and feasible community mitigation strategies. In Canada, current pandemic plans may not have been developed with adequate First Nations consultation and recommended measures may not be effective in remote and isolated First Nations communities. METHODS: This study employed a community-based participatory research approach. Retrospective opinions were elicited via interview questionnaires with adult key healthcare informants (n=9) regarding the effectiveness and feasibility of implementing 41 interventions to mitigate an influenza pandemic in remote and isolated First Nations communities of sub-Arctic Ontario, Canada. Qualitative data were manually transcribed and deductively coded following a template organizing approach. RESULTS: The results indicated that most mitigation measures could potentially be effective if modified to address the unique characteristics of these communities. Participants also offered innovative alternatives to mitigation measures that were community-specific and culturally sensitive. Mitigation measures were generally considered to be effective if the measure could aid in decreasing virus transmission, protecting their immunocompromised population, and increasing community awareness about influenza pandemics. Participants reported that lack of resources (eg supplies, monies, trained personnel), poor community awareness, overcrowding in homes, and inadequate healthcare infrastructure presented barriers to the implementation of mitigation measures. CONCLUSIONS: This study highlights the importance of engaging local key informants in pandemic planning in order to gain valuable community-specific insight regarding the design and implementation of more effective and feasible mitigation strategies. As it is ethically important to address the needs of marginalized populations, it is recommended that these findings be incorporated in future pandemic plans to improve the response capacity and health outcomes of remote and isolated First Nations communities during the next public health emergency.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | medium |
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 | 0.073 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
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".