Neurologic Music Therapy via Telehealth: A Survey of Clinician Experiences, Trends, and Recommendations During the COVID-19 Pandemic
Bibliographic record
Abstract
This cross-sectional survey investigated the transition of Neurologic Music Therapy (NMT) services from in-person (pre-COVID-19) to telehealth (since COVID-19) to (1) determine whether the use of an NMT paradigm contributes to the successful transition of therapy services to telehealth, (2) identify which NMT domains and techniques are transferable from in-person to telehealth, (3) identify whether there are differences in the transition of NMT services across different employment settings, and (4) evaluate the potential benefits and challenges of telehealth NMT. An online survey comprised of 49 closed and open-ended questions was distributed by the Academy of Neurologic Music Therapy to 2,778 NMT affiliates worldwide. The survey sought information on demographics, telehealth perceptions, technology, assessment, clinical practice, safety, and caregiver involvement. Quantitative and qualitative analyses were applied. Eighty-one participants answered the survey and the 69 who completed the survey in its entirety were included in the analysis. Results indicated that the frequency of NMT technique usage had no impact on the overall number of clinical hours retained over telehealth. Correlation analysis revealed an association between more frequent NMT usage and perceived likelihood of using telehealth in the future (i.e., once COVID-19 is no longer a major threat), as well as with fewer group sessions lost over telehealth. All NMT domains transferred to telehealth, although within the sensorimotor domain, fewer therapists implemented rhythmic auditory stimulation for telehealth sessions compared to in-person. Overall, NMTs had fewer hours for telehealth compared to in-person regardless of employment setting. Technological challenges were notable drawbacks, while major benefits included the ability to continue providing NMT when in-person sessions were not possible, increased accessibility for remote clients, and positive outcomes related to increased caregiver involvement. Based on the results, our recommendations for implementing telehealth in Neurologic Music Therapy include integrating telehealth into routine care, mitigating safety concerns, identifying those who could benefit most from remote delivery, involving caregivers, and developing/sharing resources for telehealth NMT.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".