Health Systems Responsiveness in Addressing Indigenous Residents' Health and Mental Health Needs Following the 2016 Horse River Wildfire in Northern Alberta, Canada: Perspectives From Health Service Providers
Bibliographic record
Abstract
Following the 2016 Horse River Wildfire in northern Alberta, the provincial health authority, the ministry of health, non-profit and charitable organizations, and regional community-based service agencies mobilized to address the growing health and mental health concerns among Indigenous residents and communities through the provision of services and supports. Among the communities and residents that experienced significant devastation and loss were First Nation and Métis residents in the region. Provincial and local funding was allocated to new recovery positions and to support pre-existing health and social programs. The objective of this research was to qualitatively describe the health systems response to the health impacts following the wildfire from the perspective of service providers who were directly responsible for delivering or organizing health and mental wellness services and supports to Indigenous residents. Semi-structured qualitative interviews were conducted with 15 Indigenous and 10 non-Indigenous service providers from the Regional Municipality of Wood Buffalo (RMWB). Interviews were transcribed verbatim and a constant comparative analysis method was used to identify themes. Following service provider interviews, a supplemental document review was completed to provide background and context for the qualitative findings from interviews. The document review allowed for a better understanding of the health systems response at a systems level following the wildfire. Triangulation of semi-structured interviews and organization report documents confirmed our findings. The conceptual framework by Mirzoev and Kane for understanding health systems responsiveness guided our data interpretation. Our findings were divided into three themes (1) service provision in response to Indigenous mental health concerns (2) gaps in Indigenous health-related services post-wildfire and (3) adopting a health equity lens in post-disaster recovery. The knowledge gained from this research can help inform future emergency management and assist policy and decision makers with culturally safe and responsive recovery planning. Future recovery and response efforts should consider identifying and addressing underlying health, mental health, and emotional concerns in order to be more effective in assisting with healing for Indigenous communities following a public health emergency such as a wildfire disaster.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".