Collaborative music therapy via remote video technology to reduce a veteran's symptoms of severe, chronic PTSD
Bibliographic record
Abstract
Background: Using videoconference technology to provide health care is established in many fields. The authors are not aware of any published reports of music therapy (MT) conducted remotely. This case review describes the process and outcomes of remotely delivered MT to address symptoms of post-traumatic stress disorder (PTSD) in a military veteran. Method: MT was co-facilitated by a music therapist and a clinical psychologist. Sessions were delivered as videoconferences (over 1400 km) utilizing the Ontario-Telehealth Network. A retrospective case study with input from the client was conducted. Results: The client reported improvement, in many of his symptoms. At the end of the treatment period, he attributed much of his progress to MT. Using videoconference technology did not seem to hinder the treatment efficacy. The novel nature of providing MT remotely necessitated an effective collaboration between the music therapist and the client's clinical psychologist. Conclusions: Based on the experience described in this case study, the authors concluded that, (a) remotely-delivered MT can be effective in the treatment of complex PTSD, (b) inter-professional collaboration made a positive impact on the treatment process, (c) geographic distance need not be an obstacle to effective treatment and (d) a remote treatment modality was not detrimental to treatment efficacy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".