Trainee Therapists’ Perceptions of a Blended Intervention to Promote Resilience after a Natural Disaster: A Qualitative Case Study
Bibliographic record
Abstract
BACKGROUND: Natural disasters happen in an increased frequency, and telemental health interventions could offer easily accessible help to reduce mental health symptoms experienced by survivors. However, there are very few programs offered to natural disaster survivors, and no research exists on therapists' experiences with providing blended interventions for natural disaster survivors. AIMS: Our qualitative case study aims to describe psychologists' experiences with an online, therapist-assisted blended intervention for survivors of the Fort McMurray wildfires in Alberta, Canada. METHOD: The RESILIENT intervention was developed in the frames of a randomized controlled trial to promote resilience after the Fort McMurray wildfires by providing survivors free access to a 12-module, therapist-assisted intervention, aiming to improve post-traumatic stress, insomnia, and depression symptoms. A focus group design was used to collect data from the therapists, and emerging common themes were identified by thematic analysis. RESULTS: Therapists felt they could build strong alliances and communicate emotions and empathy effectively, although the lack of nonverbal cues posed some challenges. The intervention, according to participating therapists, was less suitable for participants in high-stress situations and in case of discrepancy between client expectations and the intervention content. Moreover, the therapists perceived specific interventions as easy-to-use or as more challenging based on their complexity and on the therapist support needed for executing them. Client engagement in the program emerged as an underlying theme that had fundamental impact on alliance, communication, and ultimately, treatment efficiency. Therapist training and supervision was perceived as crucial for the success of the program delivery. CONCLUSIONS: Our findings provided several implications for the optimalization of blended interventions for natural disaster survivors from our therapists' perspective.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".