The 2009 H1N1 pandemic response in remote First Nation communities of Subarctic Ontario: barriers and improvements from a health care services perspective
Bibliographic record
Abstract
OBJECTIVES: To retrospectively examine the barriers faced and opportunities for improvement during the 2009 H1N1 pandemic response experienced by participants responsible for the delivery of health care services in 3 remote and isolated Subarctic First Nation communities of northern Ontario, Canada. STUDY DESIGN: A qualitative community-based participatory approach. METHODS: Semi-directed interviews were conducted with adult key informants (n=13) using purposive sampling of participants representing the 3 main sectors responsible for health care services (i.e., federal health centres, provincial hospitals and Band Councils). Data were manually transcribed and coded using deductive and inductive thematic analysis. RESULTS: Primary barriers reported were issues with overcrowding in houses, insufficient human resources and inadequate community awareness. Main areas for improvement included increasing human resources (i.e., nurses and trained health care professionals), funding for supplies and general community awareness regarding disease processes and prevention. CONCLUSIONS: Government bodies should consider focusing efforts to provide more support in terms of human resources, monies and education. In addition, various government organizations should collaborate to improve housing conditions and timely access to resources. These recommendations should be addressed in future pandemic plans, so that remote western James Bay First Nation communities of Subarctic Ontario and other similar communities can be better prepared for the next public health emergency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".