Potential Role of Soundscape and Music Interventions in post-Intensive Care Rehabilitation
Bibliographic record
Abstract
Background Post Intensive Care Unit (ICU) survivors face physical, psychological, and cognitive impairments during their recovery phase, adversely affecting their quality of life. Given the scarcity and barriers to access to post-ICU care worldwide, patient-centered adjuncts are worth exploring. Although music interventions have gained recognition as non-pharmacological approaches in the acute phase of critical illness; exploration of their potential benefits in the post-ICU is scarce. Objective This paper examines the potential of sound and music interventions as an adjunct therapeutic modality to improve the well-being of adults post-ICU patients, through a narrative overview of published research evidence regarding post-ICU recovery and other relevant post-acute conditions, such as post-traumatic disorder and anxiety. Results The effectiveness of sound/ music interventions on several key outcomes including reduced anxiety, stress, pain, and PTSD, improved sleep, and enhanced mood and emotional well-being have been studied. Research indicates that music-based approaches to physical and psychological well-being reduce emotional distress, foster connectedness, and improve overall well-being. The importance of tailoring sound and music interventions to the individual needs and preferences of post-ICU patients is emphasized. Conclusion The potential benefits of sound and music interventions, both in terms of physiological and psychological well-being, encourage further investigation into their potential application and implementation into post-ICU care and rehabilitation. Ultimately, this therapeutic approach could contribute to enhancing the overall quality of life and empowerment of post-ICU patients on their path to recovery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".