Hum Along With the Silent Disco Headphones: Lessons Learned in Implementing the Headphone Program in a Hospital Unit
Bibliographic record
Abstract
Abstract Silent disco headphones have been used among young people in concerts and parties; such headphones have extended distance coverage for broadcasting from a transmitter, features of noise cancelation, and three channels of music. Rather than using a speaker system, music is delivered by wireless headphones and facilitated by a DJ via a built-in microphone. No study has yet tested whether it is feasible to use such headphones to support well-being among older people in hospital settings. This study examined the feasibility of using silent disco headphones with older adults with dementia staying in a geriatric hospital unit. We employed a video-ethnographic design, including conversational interviews and observations, with video recording among ten patient participants in a hospital unit. Two focus groups were conducted with ten hospital staff across disciplines. Thematic analysis yielded three themes: (a) “it just made me feel happy, “(b) “it brings him back alive,” (c) “it unlocks dementia”. Delivering music and meditation programs via the silent disco headphones in the hospital unit has the potential to be a beneficial intervention that can enhance mood and energy, support self-expression, and promote wellness. Our findings suggested that witnessing the positive effects of headphones on patients changed the staff’s view of how music could be used in the clinical setting to support patients’ well-being. We identified enablers and barriers to implementing the headphone program in the hospital setting. Future research should further investigate how headphones may help to reduce stress and promote wellness for patients in the clinical environment.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".