The Impact of Intolerance of Uncertainty and Anxiety Sensitivity on Mental Health Among Public Safety Personnel: When the Uncertain is Unavoidable
Bibliographic record
Abstract
Abstract Background Public safety personnel (PSP; e.g., correctional workers and officers, firefighters, paramedics, police officers, public safety communications officials) are regularly exposed to potentially traumatic events and considerable uncertainty as part of their employment. Canadian PSP screen positively for mental disorders at much higher rates than the general population. Intolerance of uncertainty (IU) and anxiety sensitivity (AS) are empirically-supported vulnerability factors associated with the development and maintenance of mental disorders. Methods The present study was designed to assess IU and AS across PSP—a population regularly encountering uncertainty—with and without mental disorders ( n = 4304; 33.3% women), and across normative clinical, community, and undergraduate samples. Further, the study examined the relationship between IU and AS and mental disorders among PSP. Results There were significant differences across groups on IU and AS scores ( p s < .001). All PSP, with and without a positive screen for a mental disorder, reported lower IU and AS than clinical samples; however, PSP without mental disorders reported lower IU and AS than all other groups ( p s < .001). Conclusion Increased resilience or the development of coping skills to manage regular exposures to uncertain threat may help explain why PSP reported low levels of IU and AS despite higher prevalence of mental disorders. Implications for PSP training and treatment 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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".