Feasibility of objective psychophysiological measurement and virtual reality with Inuit in Quebec
Bibliographic record
Abstract
Emotion regulation is reflected in the reactions of the body: phenotypical patterns of autonomic nervous system (ANS) arousal like cardiac and electrodermal activity. Some data would propose that individuals who have difficulties with emotion regulation (or disorders characterized by emotion dysregulation) have a generalized over-reactivity and dysregulated recovery even after some non-trauma-related cues. Thus, psychophysiological reactivity to height could be used as a paradigm to test the dysregulation of the ANS and provide an objective measure to characterize some aspects of emotion dysregulation. This paradigm could be useful in complementing psychometric measures of mental well-being and illness, especially in populations where reliability or safety of psychometric measurement is limited due to linguistic or cultural factors. For Inuit in Quebec, the concept of emotion regulation ties closely to their ability to adapt to the environment while recognizing limited control over it and keeping hopeful for the future (e.g., resilience). Inuit have indicated that common rating scales for psychopathology are not culturally sensitive. in this case, psychophysiological measurement could be useful for both momentary assessment and in treatment (e.g., biofeedback), and could relatively easily and inexpensively be implemented through the use of virtual reality (VR) and photoplethysmography (PPG) devices. In this thesis, I describe the integration, evaluation, and testing of a reactivity testing paradigm, which aims to be a more culturally sensitive measurement. I provide both qualitative and quantitative data towards this non-trauma psychophysiological reactivity testing paradigm that uses heights to evoke both subjective (self-reported) and objective (skin conductance response and heart rate) arousal. I describe the initial results of the usefulness and feasibility of the paradigm in a sample (n=16) of healthy participants. I also outline the protocol for a future randomized controlled trial, which will test the reactivity paradigm as a complementary outcome. This work is part of a larger co-design project with an Inuit advisory committee towards culturally sensitive methods in mental health services using digital technology
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".