Towards culturally adapted virtual reality exposure therapy for Inuit in Quebec
Bibliographic record
Abstract
Suicide rates amongst Indigenous communities are 6-11 times higher than the Canadian average. Access to mental health resources in remote parts of Canada is limited. Although virtual reality (VR) is well validated as an exposure component to cognitive behavioral therapy (CBT) in anxiety disorders, including post-traumatic stress disorder, its acceptance and cultural safety among Indigenous individuals remains unknown. VR treatments that incorporate biofeedback as a way to regulate autonomic functioning may increase efficacy and in the future, could guide the process of treatment. This thesis is comprised of two major parts and is the basis for a protocol design for a future clinical trial aimed at targeting emotion regulation (ER) through a culturally adapted VR-assisted, individual CBT for Inuit of Nunavik. The first portion consists of a pilot study for this future clinical trial. Data collected to assess the feasibility of this approach was primarily through qualitative methods, from health care professionals (N=14) and key Inuit community workers. These findings confirmed that VR is readily accepted and feasible. The second part of this thesis is, through a systematic review, to determine which outcome measures are valid for the future trial. We conducted a systematic review on the utility of psychophysiological metrics as trauma intervention outcomes in order to validate the use of objective outcome measures free of cultural and linguistic elements. The findings of this review suggested that changes observed in psychophysiological measures were closely correlated to changes in the Clinician-Administered PTSD Scale (CAPs) questionnaire, a gold standard assessment of PTSD symptom severity and now the primary outcome in the protocol of the clinical trial
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".