Music Meet Up online edition: The pivot to an online music therapy group for adolescents and young adults with cancer
Bibliographic record
Abstract
The COVID-19 pandemic has heightened the unique emotional and social needs expressed by adolescents and young adults (AYAs) with cancer. To help address this, we adapted an in-person developmentally tailored group music therapy intervention to an online format. The purpose of this study was to gain preliminary data to explore the acceptability of the online version of the group. A qualitative descriptive approach was chosen to understand the experiences of program participants, who were patients at a tertiary cancer centre in Toronto, Canada. One-to-one semi-structured interviews were conducted with participants, and data was analysed using thematic analysis. A total of six interviews were completed, with five participants identifying as women. Ages ranged from 24-35, and various cancer types were represented. Overall, participants felt the online version of the program was beneficial. However, interviews pinpoint advantages and disadvantages with the online format. Delivering the program online enhanced accessibility but also created issues of disrupted/impaired “connectivity” between participants and the music. Online group psychosocial interventions, including music therapy, may continue to be offered long after the pandemic. These lessons could inform how other online music therapy interventions are delivered to AYAs between 18-39 years of age and beyond.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".