MAP: A Personalized Receptive Music Therapy Intervention to Improve the Affective Well-being of Youths Hospitalized in a Mental Health Unit
Bibliographic record
Abstract
The MAP is an innovative receptive music therapy intervention derived from psychomusical relaxation methods that aims to foster the well-being and recovery of youths with mental health problems by providing them with an adaptive and effective music-assisted means to regulate their mood states. In this quasi-experimental pilot study, we assessed the mood-enhancing potential of participation in MAP sessions delivered by a music therapist in an in-patient mental health facility for children and adolescents. Using short standardized self-reported questionnaires, 20 participants aged 9-17 years old (M = 14, SD = 2.4), mainly girls (13 = 65%), rated their affective state immediately before and after two to four MAP sessions and a similar number of regular unit activity sessions used as comparison. This created a 2 × 2 (Time × Condition) single-group within-individual design. We analyzed pre-post session changes in affect using multilevel mixed models and found participation in MAP sessions to be associated with systematic reductions in self-reported general negative affect and state anxiety. These variations were of modest-to-large magnitude and significantly greater than those associated to participation in regular unit activities. While only a first step towards the validation of the MAP as an effective intervention to foster more adaptive and effective day-to-day mood regulation in youths with mental health problems, this study supports its specific potential to alleviate negative affects and provides a rare demonstration of the putative benefits of music therapy in a pediatric mental health inpatient context.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".