A brief workplace intervention for anxiety sensitivity aimed at reducing the risk of posttraumatic stress in first responders
Bibliographic record
Abstract
First responders are repeatedly exposed to trauma in the course of their work, increasing their vulnerability to posttraumatic stress disorder (PTSD). Efforts to reduce the risk of PTSD could focus on individual factors that increase the risk for PTSD. Although many of these factors are immutable, others, such as high anxiety sensitivity (AS), can theoretically be targeted and fortified through intervention. The current study investigated the effectiveness of a brief (single session) cognitive behavioural therapy for AS intervention vs. a waitlist control in reducing AS, and subsequently mitigating PTSD and related mental health symptoms. Participants were 179 first responders from eight workplaces in Canada. The intervention was delivered remotely to these workplaces in a group format. Workplaces were randomly assigned to the intervention or waitlist control. Participants completed a self-report questionnaire pre-intervention, one-week post-intervention, and 8 months later (and at comparable time points in the control condition). Hierarchical linear regressions revealed that intervention conditions did not predict (a) AS during one-week follow-up or (b) PTSD and related symptoms during eight-month follow-up. Theoretical and clinical implications, including recommendations specific to interventions involving first responders, are discussed in detail.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".