A Sequential Mixed-Methods Study of Pre-Professionals’ Understanding of the Undergraduate Music Therapy Internship
Bibliographic record
Abstract
Despite the importance of the clinical music therapy internship, little research has been conducted to understand the perspectives, perceived musical, clinical, and personal skills, concerns, challenges, and anxieties of pre-professionals prior to and upon completion of the internship. This sequential mixed-methods study aimed to assess the perspectives and experiences of undergraduate students in the United States at two stages in the internship in music therapy practice. In total, 177 pre-professionals from the United States participated in this two-part study: (1) an online survey and (2) individual interviews with 25% (n = 44) of the participants. Survey results indicate statistically significant increases in clinical, musical, and personal skill development from pre- to post-internship. Six broad categories emerged from the interviews: confidence, anxiety, role clarity, professional suitability, loneliness, and boundaries/ethics. The results are encouraging, showing that the internship is a valued clinical experience that fosters improvement in the clinical, musical, and personal skills needed to work as a music therapist. This paper concludes with recommendations and suggestions for educators and supervisors on preparing and supporting pre-professionals in their skill development prior to and during internship.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".