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Record W2126009284 · doi:10.1123/japa.16.4.435

Effect of a Community-Based Argentine Tango Dance Program on Functional Balance and Confidence in Older Adults

2008· article· en· W2126009284 on OpenAlexafffund
Patricia McKinley, Allison Jacobson, Alain Leroux, Victoria Bednarczyk, Michel Rossignol, Joyce Fung

Bibliographic record

VenueJournal of Aging and Physical Activity · 2008
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsDanceBalance (ability)PsychologyGerontologyPhysical medicine and rehabilitationMedicineArtVisual arts

Abstract

fetched live from OpenAlex

Tango-dancing and walking programs are compared in nondemented seniors at risk for falls. Fallers (N = 30) age 62-91 were randomly assigned to a 10-wk (40 hr, 2 hr 2x/wk) tango class or walk group. The Activities-specific Balance Confidence (ABC) scale, sit-to-stand scores, and normal and fast walk were measured pre-, post-, and 1 month postintervention. Two-way repeated-measures ANOVAs indicated a significant main effect (p < .01) for time on all measures. Group and interaction effects for ABC led to improvement only in tango because of high baseline mean for the walk group. Clinical improvements measured using Established Populations for Epidemiologic Studies of the Elderly scoring were greater for the tango group. From these preliminary results it is suggested that although both interventions are effective activities for increasing strength and walk speed, tango might result in greater improvements than walking in balance skills and in walking speed in the 10-wk intervention. The study needs to be repeated with a greater sample size to determine the effectiveness of walking on fear of falling.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.361
Teacher spread0.334 · 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

Citations147
Published2008
Admission routes2
Has abstractyes

Explore more

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