Music and hypnosis for well-being in retirement homes: A pilot study
Bibliographic record
Abstract
Anxiety, pain and poor well-being are major issues in elderly individuals. Complementary interventions, such as music and hypnosis, are increasingly used to address these issues. The objectives of this study were to assess immediate changes in anxiety, pain and well-being during personalized prerecorded music and hypnosis interventions compared to control sessions, and to explore participants’ subjective experiences. We employed a multiple time series model with daily measurements with older people living in retirement homes in rural areas (n = 8). The Edmonton Symptom Assessment Scale (ESAS) evaluated these three dimensions before and after each session, while participants’ subjective experience was collected using an unstructured interview. The primary results showed a significant improvement in the composite score of anxiety, pain, and well-being for the music (p < .001), hypnosis (p = .0158), and music/hypnosis (p = .013) intervention sessions compared to the control sessions. The secondary results indicated a reduction in anxiety for both the music and music/hypnosis interventions (p < .05), along with a significant improvement in well-being. These effects may be attributed to mechanisms such as absorption, episodic memory, cognitive agency, positive emotion, rhythmic entrainment, and rapport, which could have modulated the interventions’ impact. In conclusion, personalized prerecorded music and hypnosis interventions appear to be effective in enhancing the well-being of older individuals residing in retirement homes. Further studies are needed to assess the generalizability of these results to a larger population from diverse sociodemographic backgrounds, and better understand the subjective experiences that mediate these effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".