Impact of The HEROES Project on First Responders’ Well-Being
Bibliographic record
Abstract
First responders experience a myriad of stressors (e.g., operational, organizational, personal) over the course of their career. An abundance of empirical evidence shows that the impact of those stressors on first responders’ health, well-being, and performance can be detrimental. Nevertheless, previous research has mainly focused on the role of a specific technique (e.g., mindfulness, breathing exercises, psychoeducation) towards the promotion of well-being among first responders. This allows us to explore the role of a single technique in supporting first responders. However, given the complexity of stressors experienced by this population, it appears that a synergistic role of multileveled intervention is imperative to promote lasting improvement in first responders’ well-being. To this end, The HEROES Project, an eight-week online training program, was developed to address the aforementioned gap in the literature. The HEROES Project incorporates lessons that aim to build a cluster of skills that together promote first responders’ wellbeing. In the present study, a sample of first responders (n = 124) from the US Midwest were recruited and completed The HEROES Project. They were assessed before and after completion of the program, and then follow-up measurements were obtained for two years following the baseline assessment. Results showed that participants with higher distress and lower psychological resources before the training benefited most from The HEROES Project, but that the training significantly improved psychological capital and reduced stress, depression, anxiety, and trauma symptoms for all participants. Clinical and training implications as well as future research directions are discussed.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".