Evaluating the 3-month post-intervention impact of a supportive text message program on mental health outcomes during the 2023 wildfires in Alberta and Nova Scotia, Canada
Bibliographic record
Abstract
Background Individuals exposed to wildfires are at risk of developing adverse mental health conditions in the months following the event. Receiving supportive text interventions during and after a wildfire event can have a significant impact on reducing mental health conditions over time. Objectives The study aimed to assess the effectiveness of a supportive text message intervention service in reducing the severity and prevalence of psychological conditions 3 months following the 2023 wildfires in Alberta and Nova Scotia, two regions heavily affected by these natural disasters. Methods In this longitudinal study, participants voluntarily subscribed to the Text4Hope-AB and Text4Hope-NS services, receiving supportive text interventions for 3 months. On enrolment and at 3 months post-enrolment, participants completed online surveys. The severity and prevalence of mental wellbeing, resilience, depression, anxiety, and post-traumatic stress were measured using the World Health Organization- Five Well-Being Index (WHO-5), Brief Resilience Scale (BRS), Patient Health Questionnaire 9 (PHQ-9), Generalized Anxiety Disorder - 7 scale (GAD-7), and Post-Traumatic Stress Disorder Checklist for Civilians (PCL-C) respectively. Data analysis involved using McNemar’s chi-square test and paired sample t-tests. Results A total of 150 subscribers partially or fully completed both the baseline and 3-month assessments. The results show a statistically significant change in the mean scores on the WHO-5 Wellbeing Index (+ 24.6%), PHQ-9 (−17.0%), GAD-7 scale (−17.6%), PCL-C (−6.0%), and BRS (+3.2%) from baseline to 3 months. Similarly, there was a reduction, although not statistically significant, in the prevalence of low resilience (55.1 vs. 53.4%), poor mental well-being (71.6 vs. 48.3%), likely MDD (71.4 vs. 40.7%), likely GAD (42.1 vs. 33.3%), and likely PTSD (42.0 vs. 38.4%). Conclusion The study’s findings underscore the potential of the supportive text intervention program in effectively aiding individuals who have endured natural disasters such as wildfires. Providing supportive text messages during wildfire events is a promising strategy for mitigating mental health conditions over time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".