Pilot Study Exploring the Perspectives of Canadian Clients Who Received Digitally Delivered Psychotherapies Utilized for Trauma-Affected Populations
Bibliographic record
Abstract
The digital delivery of mental health services became increasingly common following the onset of the COVID-19 pandemic. There is still much to learn regarding tailoring interventions for trauma-affected populations (military members, Veterans, public safety personnel). Through the current pilot study, we explored the perceptions of digitally delivered psychotherapies utilized for trauma-affected populations, as reported by Canadian military members, Veterans, and public safety personnel who completed such interventions. Quantitative data were collected from 11 Canadian clients (military members, Veterans, and public safety personnel with posttraumatic stress injury). Survey questions were based on the Alberta Quality Matrix of Health and the Unified Theory of Acceptance and Use of Technology model. As a follow-up, clients were invited to partake in a semi-structured interview to further explore their perspectives on digitally delivered trauma-focused and adjunct therapies. Four clients participated in an interview. The client participants reported that digitally delivered trauma and adjunct therapies offered similar treatment effectiveness to in-person delivery while also improving treatment access. The participants indicated several unique advantages of digital delivery, including the increased accessibility of treatment, cost-effectiveness, and more efficient use of resources, although the small sample size limits the generalizability of our findings. Further research with a larger, more diverse population is required to corroborate our results and identify other avenues in which psychotherapies utilized for trauma-affected populations can be engaged with and improved upon.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".