Exploring EMDR with trauma-impacted clients using video therapy
Bibliographic record
Abstract
Eye Movement Desensitization & Reprocessing (EMDR) is an 8-phase psychotherapy approach that has been proven effective with various populations and presenting client issues, including and beyond the treatment of Post-Traumatic Stress Disorder (Ehring et al, 2014, Valiente-Gomez et al, 2017, Edmond, Rubin & Wambach, 1999, Yunitri et al, 2020, Gerge, 2020, Schwarz et al, 2019, Cuijpers et al, 2020). However, existing research focuses on validating the claims of EMDR, rather than examining its power of engagement and the motivations behind its growing use with practitioners. In addition, with the onset and continuation of the COVID-19 pandemic, therapy modalities such as EMDR have been presented with the challenge of adapting treatment to video-based services. This project explored the specific strategies and experiences of therapists using EMDR and how they construct an understanding of its value in the field of trauma treatment. Within this project, five Winnipeg-based EMDR clinicians participated in interviews, analyzed using Riessman’s “Analysis of Personal Narratives” (2000), and findings were organized into themes of qualities of a strong therapist, determining appropriate EMDR services, video-specific considerations, working with differences and oppression, and supports for therapist efficacy. This research examines the processes and strategies that create meaning for EMDR practitioners and contributes to a larger discussion about the challenges and strengths of trauma treatment in the current social climate.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".