Recent Unprecedented Wildfires in British Columbia, Canada: Progression of a Grassroots Disaster Psychosocial Program
Bibliographic record
Abstract
Introduction: Psychosocial needs related to disaster are increasingly identified as a significant concern for both communities and responders. In response to the needs of travelers suddenly unable to leave Vancouver immediately after 9/11 in the United States, a network for the provision of volunteer mental health response at the time of a disaster was developed through the Provincial government within British Columbia (BC). Starting from less than 20 individuals primarily located within the Vancouver area, Disaster Psychosocial Services (DPS) now encompasses a network of over approximately 200 providers throughout the Province. Aim: To showcase a successfully functioning DPS program modeled after a volunteer-based mental health network, the evolution undergone, its present operational framework, and future goals. Methods: In response to the observed need for trained psychosocial intervention, we developed a framework for recruitment, education, deployment, and support of a volunteer network of mental health professional and paraprofessional providers. Results: This approach has been found to be effective, significantly increasing our volunteer base and opportunities for deployment. Discussion: This presentation will detail the grassroots development of BC’s DPS Program as well as the current model in practice. It will provide an overview of how BC’s DPS network of providers was stimulated and managed; issues related to volunteer management, including the selection of volunteers; methods of specialized training; and deployment. Multiple settings in which DPS is now utilized with increasing regularity will be described, including Emergency Operations Centers, Reception Centers, and Town Hall Meetings. Lastly, there will be a focus on the lessons learned, as well as future goals highlighting a focus on culturally-sensitive support, specifically with respect to British Columbia’s indigenous populations for building community resiliency and knowledge across the province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".