Exploring Perceptions of Internet-Delivered Cognitive Behaviour Therapy among Public Safety Personnel: Informing Dissemination Efforts
Bibliographic record
Abstract
Background Public safety personnel (PSP) experience high rates of mental health disorders but have limited access to treatment. To improve treatment access, there is a growing interest in offering internet-delivered cognitive behaviour therapy (ICBT) to PSP. As attitudes towards ICBT can both impact and inform ICBT implementation efforts, this study examines perceptions of ICBT among PSP who viewed a poster (a commonly used method of advertising ICBT) or a poster supplemented with a story of a PSP who benefitted from ICBT. Methods Participants (N = 132) from various PSP sectors were randomly assigned to view a poster or a poster and a story. Participants then completed an online survey assessing their perceptions of ICBT using both qualitative and quantitative questions. We used a mixed-methods approach to analyze the data. Results No differences in perceptions of ICBT were identified between the conditions. Ratings of credibility, treatment expectancy, anticipated treatment adherence, and acceptability suggested that PSP had positive perceptions of ICBT. Most participants (93%) reported that they would access ICBT if they needed help with mental health concerns. Participants ranked therapist-guided ICBT as their second most preferred treatment, with psychologists ranked first. Female participants found ICBT more credible than male participants. More experienced PSP reported lower acceptability and anticipated adherence to ICBT. Conclusions The findings suggest that many PSP are likely to be receptive to ICBT even when a simple poster is used as a method of informing PSP of this treatment option. Further attention to improving the perceptions of ICBT among certain groups may be warranted.
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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 | 0.032 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".