Preliminary findings from the early phases of the Music and Movement for Health study: the feasibility of an arts-based health programme for older adults
Bibliographic record
Abstract
Introduction (including aim): There is a lack of community-based programmes for older adults in Ireland. Such activities are vital to enable older people to (re)connect after COVID-19 measures, which had a detrimental effect on physical function, mental health and socialisation. The aims of the preliminary phases of the Music and Movement for Health study were to refine stakeholder informed eligibility criteria, recruitment pathways and obtain preliminary measures for feasibility of the study design and programme, which incorporates research evidence, practice expertise and participant involvement. METHODS: Two Transparent Expert Consultations (TECs) (EHSREC No: 2021_09_12_EHS), and Patient and Public Involvement (PPI) meetings were conducted to refine eligibility criteria and recruitment pathways. Participants from three geographical regions in the mid-west of Ireland will be recruited and randomised by cluster to participate in either a 12-week Music and Movement for Health programme or control. We will assess the feasibility and success of these recruitment strategies by reporting recruitment rates, retention rates and participation in the programme. RESULTS: Both the TECs and PPIs provided stakeholder-informed specification on inclusion/ exclusion criteria and recruitment pathways. This feedback was vital in strengthening our community-based approach as well as effecting change at the local level. The success of these strategies from phase 1 (March-June) are pending. DISCUSSION: Through engaging with relevant stakeholders, this research aims to strengthen community systems by embedding feasible, enjoyable, sustainable and cost-effective programmes for older adults to support community connection and enhance health and wellbeing. This will, in turn, reduce demands on the healthcare system.Note: We would like to thank and acknowledge those who participated in the PPIs for their time and invaluable feedback.
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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 | 0.097 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".