Factors Influencing Music Therapists’ Retention of Clinical Hours with Autistic Clients over Telehealth During the COVID-19 Pandemic
Bibliographic record
Abstract
The 2019 coronavirus disease pandemic influenced music therapists to migrate services to online platforms, though some lost clinical hours during the pandemic when telehealth was not a viable option. This survey study aimed to ascertain factors that helped music-based therapists to continue serving autistic clients over telehealth during the pandemic. We surveyed 193 accredited music therapists located mainly in Canada and the US. In addition to gathering data on general perceptions of telehealth music therapy and Neurologic Music Therapy (NMT), one-way ANOVAs were applied to determine differences in percent-change loss of clinical hours for music therapists: (1) working in different employment settings; (2) serving children, youth, adults, or a mixture of ages; and (3) practicing NMT or not. The general perception of telehealth music therapy was positive, and NMTs believed that the clear protocols and transformation design model were helpful to them in adapting services to telehealth. There were no significant differences in percent-change of clinical hours among music therapists in different employment settings or serving different client age groups. Music therapists who said they practiced within the NMT treatment model lost a significantly lower percentage of clinical hours with autistic clients than those who did not practice NMT. Possible reasons for this result and the need for further research are discussed.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".