A Survey Study of Pre-Professionals' Understanding of the Canadian Music Therapy Internship Experience
Bibliographic record
Abstract
BACKGROUND: There is limited research to date on the clinical music therapy internship experience from the perspective of the pre-professional. Further study is required to advance this significant stage in clinician development, as it is an intense period when pre-professionals apply and integrate theoretical knowledge about music therapy into their clinical practice. OBJECTIVE: This study aimed to: (1) assess the skills, competence, comfort, concerns, issues, challenges, and anxieties of Canadian undergraduate students at two stages in the internship process (pre- and post-internship); and (2) examine whether these perceptions are consistent with published research on internship. METHODS: Thirty-five pre-professionals, from a pool of 50 eligible respondents (70% response rate), completed a 57-question survey using a five-point Likert scale ranking pre- and post-internship experience and participated in an interview post-study. RESULTS: Survey results indicate a statistically significant increase in pre-professionals' perceived clinical, music, and personal skill development from pre- to post-internship. Areas of desired skill development included counseling, functional guitar, and clinical improvisation. CONCLUSIONS: Recommendations for educators and supervisors are provided with respect to areas of focus in undergraduate education and during clinical 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".