Outcomes of Internet-Delivered Cognitive Behavioural Therapy Tailored for Canadian Public Safety Personnel Among Indigenous and White Clients
Bibliographic record
Abstract
Indigenous Peoples and public safety personnel are two groups that report very high rates of mental health challenges. Internet-delivered cognitive behavioural therapy is an effective treatment for various mental health challenges with promising results among Indigenous clients and public safety personnel. However, research on the mental health of Indigenous public safety personnel and the treatment of mental health challenges among Indigenous public safety personnel is extremely scarce. In the current study—using questionnaire responses and program usage data—we compared outcomes of internet-delivered cognitive behavioural therapy (i.e., symptom change, program use, and treatment satisfaction) among 35 Indigenous and 356 White public safety personnel in Canada (N = 391) to assess whether Indigenous public safety personnel benefit similarly to White public safety personnel from internet-delivered cognitive behavioural therapy. Data analyses included multiple imputation, chi-square analyses, independent-samples t-tests, and ANCOVAs for various outcomes. We also present several illustrative quotes from Indigenous clients. Our results show elevated pre-treatment symptoms among Indigenous public safety personnel but no differences between ethnic groups with respect to symptom change, program use, or treatment satisfaction. Our findings provide preliminary evidence that internet-delivered cognitive behavioural therapy can be effective among Indigenous public safety personnel. However, we discuss several important caveats, limitations, and considerations for future efforts to develop, adapt, deliver, and evaluate psychological interventions for Indigenous public safety personnel and other Indigenous populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".