Development and integration of a music therapy program in the neurologic inpatient setting: a qualitative study
Bibliographic record
Abstract
PURPOSE: Explore facilitators and barriers to development and integration of an inpatient music therapy (MT) program from the perspective of the patient, family member, and health care professional. MATERIALS AND METHODS: This qualitative study recruited patients on acute neurosciences/neurorehabilitation units having participated in the hospital MT program, their family, and members of their health care team. Semi-structured individual interviews and focus groups were conducted with 35 participants (14 patients, 5 family members, 16 health care professionals). Interviews/focus groups were audio recorded and transcribed verbatim. Data were coded in duplicate and a codebook was developed through an iterative process. RESULTS: Four dominant themes emerged from the data: (1) facilitators of program operations; (2) barriers to program establishment; (3) perceived positive impact on patient outcomes; and (4) opportunity for improvement. Facilitator sub-themes included a love for music that encouraged participation, broad appeal of MT, and support of the health care team. CONCLUSIONS: Patients, health care professionals, and family members accepted MT as a treatment modality. While there is growing evidence for MT in neurorehabilitation, practical challenges remain in developing inpatient MT services, including funding, and optimal integration of music therapists into existing care teams.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".