Initial Outcomes of Transdiagnostic Internet-Delivered Cognitive Behavioral Therapy Tailored to Public Safety Personnel: Longitudinal Observational Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Canadian public safety personnel (PSP) experience high rates of mental health disorders and face many barriers to treatment. Internet-delivered cognitive behavioral therapy (ICBT) overcomes many such barriers, and is effective for treating depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms. OBJECTIVE This study was designed to fill a gap in the literature regarding the use of ICBT tailored specifically for PSP. We examined the effectiveness of a tailored ICBT program for treating depression, anxiety, and PTSD symptoms among PSP in the province of Saskatchewan. METHODS We employed a longitudinal single-group open-trial design (N=83) with outcome measures administered at screening and at 8 weeks posttreatment. Data were collected between December 5, 2019 and September 11, 2020. Primary outcomes included changes in depression, anxiety, and PTSD symptoms. Secondary outcomes included changes in functional impairment; symptoms of panic, social anxiety, and anger; as well as treatment satisfaction, working alliance, and program usage patterns. RESULTS Clients reported large symptom reductions on measures of depression and anxiety, as well as moderate reductions on measures of PTSD and secondary symptoms, except for social anxiety. Most clients who reported symptoms above clinical cut-offs on measures of depression, anxiety, and PTSD during screening experienced clinically significant symptom reductions. Results suggested good engagement, treatment satisfaction, and working alliance. CONCLUSIONS Tailored, transdiagnostic ICBT demonstrated promising outcomes as a treatment for depression, anxiety, and PTSD among Saskatchewan PSP and warrants further investigation. CLINICALTRIAL Clinicaltrials.gov NCT04127032; https://www.clinicaltrials.gov/ct2/show/NCT04127032
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".