EFFECTS OF MUSIC THERAPY ON PSYCHOLOGICAL DISTRESS OF NEUROSURGICAL PATIENTS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Neurosurgery has been on the rise, with a yearly estimate of 13.8 million patients suffering from neurological disorders or injuries and require surgery. Psychological distress is relatively frequent in neurosurgical patients due numerous threats and challenges faced therefore, the main objective of this review is to understand the efficiency of music therapy on neurosurgical patients in reducing psychological distress. The Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) framework was used to guide the methodology of this systematic review. The PICO format was used as a search strategy in terms of specifying search terms and clarifying limits in relation to the population or intervention studied in this review. Databases like SCOPUS, MEDLINE and OVID, The Cochrane Library was utilized to search for relevant records. A total of 48 studies were identified through the databases search. After the removal of duplicates, 39 studies’ titles and abstracts were screened. Through a process of assessing eligibility, 5 studies were consequently included in the review. The year limits for the articles reviewed were 2015 to present, to highlight the more recent findings on the subject. The studies included in this review encompass different countries of origin such as USA, Canada and in Asia, Taiwan, China and India. The findings of this review show that music therapy is an effective intervention in reducing psychological distress, especially anxiety, in neurosurgical patients. Music therapy is also effective as an adjunct therapy for neurosurgical procedures. The cultural aspects infused in music therapy were also discussed in this paper
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".