Exploring the Perspectives of Canadian Clinicians Regarding Digitally Delivered Psychotherapies Utilized for Trauma-Affected Populations
Bibliographic record
Abstract
Many clinical sites shifted towards digital delivery of mental health services during the COVID-19 pandemic. There is still much to learn regarding tailoring digitally delivered interventions for trauma-affected populations. The current study examined the perceptions of Canadian mental health clinicians who provided digitally delivered psychotherapies utilized for trauma-affected populations. Specifically, we explored the shift to digital health use, what changed with this rapid shift, what needs, problems, and solutions arose, and important future considerations associated with delivering trauma-focused and adjunct treatments digitally. Survey data were collected from 12 Canadian mental health clinician participants. Surveys were adapted from the Alberta Quality Matrix of Health and Unified Theory of Acceptance and Use of Technology model. As a follow-up, the participants were invited to participate in either a semi-structured qualitative interview or focus group to further explore their perspectives on digitally delivered trauma-focused and adjunct therapies. Twenty-four clinician participants partook in an interview or focus group. The participants in this study supported the use of digitally delivered psychotherapies utilized for trauma-affected populations, sharing that these interventions appeared to offer similar quality of care to in-person delivery. Further research is required to address clinicians' concerns with digital delivery (e.g., patient safety) and identify other avenues in which digitally delivered 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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".