Nurturing Spiritual Resilience to Promote Post-disaster Community Recovery: The 2016 Alberta Wildfire in Canada
Bibliographic record
Abstract
The 2016 Alberta wildfire, the largest insured natural disaster in Canada, led to a mass evacuation of residents of Fort McMurray, a small city in northern Alberta. The wildfire resulted in significant damages to housing and community infrastructure. The entire community was displaced for several weeks. Post-disaster, community members experienced individual and collective trauma, and other negative mental health impacts in response to the significant losses and grief they endured. Spirituality has been found to be a major protective factor in facilitating resiliency and recovery following the experience of disaster. Nonetheless, little focus has been directed toward how spirituality can strengthen and empower community capacity and growth during post-disaster recovery. Our study explored various meanings and concerns, along with tools and strategies that helped to nurture spiritual resilience and well-being among residents of Fort McMurray following the Alberta wildfire. Data were collected through interviews and focus group discussions with community influencers working to support long-term recovery efforts in the city. Participants identified a number of spiritual resources such as a strong sense of belonging, a shared positive outlook, faith and hope, compassion, and sense of gratitude, which contributed to increased resilience and positive health and well-being and helped them to support families and communities in the post disaster recovery period. Our findings indicate that spiritual values and beliefs can play a significant role in building resilience and promoting individual and communal healing and recovery post-disaster. These findings have important implications for post-disaster recovery strategies, as they highlight the need to ensure supports for interventions and initiatives that strengthen a collective sense of identity and social cohesion, informed by communal norms and beliefs, including programs and resources which support opportunities for reflexivity to foster shared healing and ongoing recovery processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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, 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".