Why Do Public Safety Personnel Seek Tailored Internet-Delivered Cognitive Behavioural Therapy? An Observational Study of Treatment-Seekers
Bibliographic record
Abstract
First responders and other public safety personnel (PSP) experience elevated rates of mental disorders and face unique barriers to care. Internet-delivered cognitive behavioural therapy (ICBT) is an effective and accessible treatment that has demonstrated good treatment outcomes when tailored specifically for PSP. However, little is known about how PSP come to seek ICBT. A deeper understanding of why PSP seek ICBT can inform efforts to tailor and disseminate ICBT and other treatments to PSP. The present study was designed to (1) explore the demographic and clinical characteristics, motivations, and past treatments of PSP seeking ICBT, (2) learn how PSP first learned about ICBT, and (3) understand how PSP perceive ICBT. To address these objectives, we examined responses to online screening questionnaires among PSP (N = 259) who signed up for an ICBT program tailored for PSP. The results indicate that most of our sample experienced clinically significant symptoms of multiple mental disorders, had received prior mental disorder diagnoses and treatments, heard about ICBT from a work-related source, reported positive perceptions of ICBT, and sought ICBT to learn skills to manage their own symptoms of mental disorders. The insights gleaned through this study have important implications for ICBT researchers and others involved in the development, delivery, evaluation, and funding of mental healthcare services for PSP.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".