Government bodies and their influence on the 2009 H1N1 health sector pandemic response in remote and isolated First Nation communities of sub-Arctic Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: First Nation communities were highly impacted by the 2009 H1N1 influenza pandemic. Multiple government bodies (ie federal, provincial, and First Nations) in Canada share responsibility for the health sector pandemic response in remote and isolated First Nation communities and this may have resulted in a fragmented pandemic response. This study aimed to discover if and how the dichotomy (or trichotomy) of involved government bodies led to barriers faced and opportunities for improvement during the health sector response to the 2009 H1N1 pandemic in three remote and isolated sub-arctic First Nation communities of northern Ontario, Canada. METHODS: A qualitative community-based participatory approach was employed. Semi-directed interviews were conducted with adult key informants (n=13) using purposive sampling of participants representing the two (or three) government bodies of each study community. Data were manually transcribed and coded using deductive and inductive thematic analysis to reveal positive aspects, barriers faced, and opportunities for improvement along with the similarities and differences regarding the pandemic responses of each government body. RESULTS: Primary barriers faced by participants included receiving contradicting governmental guidelines and direction from many sources. In addition, there was a lack of human resources, information sharing, and specific details included in community-level pandemic plans. Recommended areas of improvement include developing a complementary communication plan, increasing human resources, and updating community-level pandemic plans. CONCLUSIONS: Participants reported many issues that may be attributable to the dichotomy (or trichotomy) of government bodies responsible for healthcare delivery during a pandemic. Increasing formal communication and collaboration between responsible government bodies will assist in clarifying roles and responsibilities and improve the pandemic response in Canada's remote and isolated First Nation 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".