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Record W3077505525 · doi:10.1080/17533015.2020.1802606

Exploring the impact of a clinical dance therapy program on the mobility of adults with a neurological condition using a single-case experimental design

2020· article· en· W3077505525 on OpenAlexaff
Brigitte Lachance, Sylvie Fortin, Nathalie Bier, Bonnie Swaine

Bibliographic record

VenueArts & Health · 2020
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à MontréalUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsDancePsychological interventionDance therapyPhysical therapyTest (biology)PsychologyPhysical medicine and rehabilitationRepeated measures designResearch designMedicinePsychiatryVisual artsArt

Abstract

fetched live from OpenAlex

BACKGROUND: This study built upon previous quasi-experimental design research studying the effectiveness of a 12-week dance therapy program for persons with a physical disability (DTPD) aiming to improve mobility. METHODS: We conducted a single-case experimental design (SCED), including pre- and post-interventions measures, with seven participants with repeated measures during pre-dance (A1), dance program (B) and post-dance phases (A2). RESULTS: Five participants completed the study and significantly (p < 0.05) improved their scores on the MiniBESTest; 2/5 and 4/5 improved scores for the 4 Square Step Test and the Multidirectional Reach Test-Behind, respectively, with very large effect size (ES). Aggregated ES (A1-A2) went from moderate to very large. CONCLUSIONS: Results support the effectiveness of the DTPD program for adults with neurological conditions, and for the use of SCED to explore effectiveness of dance interventions for heterogeneous cohorts.

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)0.010
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.677
GPT teacher head0.482
Teacher spread0.195 · 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 designNon-randomized trial
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

Citations3
Published2020
Admission routes1
Has abstractyes

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