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Record W3198767343 · doi:10.1123/apaq.2020-0133

Singing and Dancing With Neuromuscular Conditions: A Mixed-Methods Study

2021· article· en· W3198767343 on OpenAlexaff
Danielle Peers, Lindsay Eales, Kelvin E. Jones, Aidan L. Toth, Hernish Acharya, Janice Richman–Eisenstat

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSingingSwallowingPsychologyNeuromuscular diseasePhysical medicine and rehabilitationThematic analysisQuality of life (healthcare)ChokingPhysical therapyMedicineQualitative research

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the safety and meaningfulness of a 15-week recreational dance and singing program for people with neuromuscular conditions. Within a transformative mixed-methods design, pulmonary function tests, plethysmography through wearable technology (Hexoskin vests), individualized neuromuscular quality-of-life assessments (version 2.0), and semistructured interviews were used. The interviews were analyzed through inductive, semantic thematic analysis. Although the sample sizes were small (six people with neuromuscular conditions), the authors found no evidence of safety concerns. There was evidence of respiratory improvements and reported improvements in swallowing and speech. The most notable quality-of-life changes included improvements related to weakness, swallowing, relationships, and leisure. The participants shared that the program offered meaningful social connection and embodied skills and safe and pleasurable physical exertion. The authors learned that recreational singing and dancing programs could be a safe and deeply meaningful activity for those with neuromuscular conditions that impact respiration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.423
Teacher spread0.394 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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