Sustaining and Expanding Internet-Delivered Cognitive Behavioral Therapy (ICBT) for Public Safety Personnel across Canada: A Survey of Stakeholder Perspectives
Bibliographic record
Abstract
Public safety personnel (PSP) experience an elevated risk of mental health problems and face barriers to treatment. Internet-delivered cognitive behavioral therapy (ICBT) has been tailored to PSP to improve access to mental health care. In this study, we sought to investigate perceptions of ICBT, particularly among those with and without prior knowledge of ICBT and between PSP leaders and non-leaders. A survey was administered to 524 PSP from across Canada to identify (a) how PSP perceive ICBT, (b) the extent of organizational support for tailored ICBT in PSP organizations, particularly leadership's support, and (c) perceived facilitators and barriers to funding tailored ICBT. The results indicated that PSP perceive ICBT to have more advantages than disadvantages. PSP who had previously heard of tailored ICBT had more positive perceptions. PSP indicated that there is a need for ICBT, and PSP leaders indicated their support for the implementation of tailored ICBT. The study identified that there is a need for increasing awareness of the effectiveness of and need for ICBT in order to facilitate funding of services. Overall, the current study indicates that PSP support ICBT as a valued form of therapy and that policy makers and service providers seeking to provide ICBT to PSP may increase support for ICBT services through more education and awareness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".