Use of headphones for the delivery of music programs for people with dementia in long-term care homes: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Dementia affects the quality of life. Excessive noise in care environments can exacerbate stress and related symptoms. Headphone-based music interventions may help improve the quality of life for people with dementia in long-term care homes. This review aims to explore and synthesise research on headphone-based music interventions for people with dementia in long-term care homes, focusing on enablers and barriers to implementing headphone-based music interventions. METHODS AND ANALYSIS: Joanna Briggs Institute guidance for scoping review and Preferred Reporting Items for Scoping Reviews and Meta-analyses extension for Scoping Reviews will guide the review and report process. CINAHL, MEDLINE, Embase, Web of Science, Scopus, AgeLine, PsycINFO and ProQuest databases will be searched for relevant literature from June 2010 to January 2024, supplemented by hand searches and Google for grey literature. Two research assistants will independently screen citations, followed by a full-text review. Data will be extracted using a data extraction tool. We will present the data in a table with narratives that answer the questions of the scoping review. ETHICS AND DISSEMINATION: This scoping review does not require ethics approval and participation consent, as all data will be publicly available. The scoping review results will be disseminated through conference presentations and an open-access publication in a peer-reviewed journal. The findings will provide practical insights into the adoption and efficacy of headphone-based music programmes for dementia in long-term care homes, contributing to education, practice, policy and future research.
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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 | 0.085 | 0.063 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.016 |
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".