Challenges faced by health policymakers responding to COVID-19 in remote communities in Northwest Territories, Canada
Bibliographic record
Abstract
The health systems' response to the COVID-19 pandemic controlled the virus's spread but exposed fragmented systems and operational challenges globally. Understanding these issues is essential for enhancing health system capabilities and improving future pandemic responses. This study explored the perspectives of health policymakers in Northwest Territories (NWT), Canada, on the challenges to responding to COVID-19 and implementing the necessary public health measures in the jurisdiction. This study utilized a qualitative descriptive design and a community-based participatory research approach. Framework analysis, guided by the World Health Organization's Health Emergency and Disaster Risk Management framework, was used for data analysis. The Consolidated Criteria for Reporting Qualitative Research were followed. Convenience sampling was used to recruit policymakers working in NWT. Data were collected between June and August 2021 from 65 policymakers using semi-structured interviews. Participants worked in territorial (71.7%), regional (14.3%), and community (14.3%) organizations. Four themes were identified: governance (e.g. unclear roles and responsibilities, policy lag, and limited community consultation), public risk communication (e.g. complexity of information and language barriers), community-level (e.g. community's distrust of the health system and geographic barriers), and health system challenges (e.g. limited human resource capacity and material resources, absence of robust information systems). To build a resilient health system in NWT for future pandemics, it is essential to define roles and responsibilities, collaborate with healthcare providers and community leaders, develop efficient data infrastructure, and enhance system capacity. Effective communication and fostering trust between the government and communities are important.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.006 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".