The Online Conference for Music Therapy (OCMT): Demonstrating best practices for virtual conferences, education and training
Bibliographic record
Abstract
This report highlights the Online Conference for Music Therapy (OCMT) and assesses the impact of the global pandemic COVID-19 on the education and training of music therapy students, as well as music therapy and music and medicine conferences. Virtual conferences and the future of conferences in general are overviewed, while best practices for virtual conferences are sharedand connected to the OCMT exemplar.With considerable uncertainty regarding the long-term impact of the pandemic on face-to-face conferences and instruction, it does seem timely for a review or study of the feasibility of teaching music therapy courses online versus in class. With each online conference new knowledge is gained and best practices will continue to evolve. Given COVID-19, it is timely that the profession addresses the benefits and challenges of remote learning and telehealth practices for music therapy training. The music therapy community is fortunate to have the experience and practices of the OCMT to inform their virtual events during this pandemic.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".