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Record W4417427897 · doi:10.1016/j.jarlif.2025.100046

Exploring balance challenge in fall prevention community exercise programs for older adults across Canada: A cross-sectional electronic survey of instructor perceptions

2025· article· en· W4417427897 on OpenAlexafffundabout
Alison Bulow, Alexie J. Touchette, Alison Oates, Kathryn M. Sibley

Bibliographic record

VenueJournal of Aging Research and Lifestyle · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsGeorge & Fay Yee Centre for Healthcare InnovationUniversity of SaskatchewanUniversity of Manitoba
FundersCanada Research Chairs
KeywordsFall preventionBalance (ability)PerceptionBalance trainingPoison controlHuman factors and ergonomics

Abstract

fetched live from OpenAlex

Background: Exercise that challenges balance helps reduce falls in older people. Evaluating the intensity of balance challenge is difficult and no validated measures exist for group settings. Objective: To examine how instructors determine and perceive balance challenge at the program level, and explore relationships between estimates of program-level balance challenge. Design: Cross-sectional self-report study. Setting: Electronic survey questionnaire approach. Participants: Instructors of Canadian group exercise programs targeting community-dwelling older adults. Measurements: Instructors perceived program-level balance challenge and estimates of program-level balance challenge. Results: = 4, 80%) had no relationship to perception of balance challenge. Conclusions: Findings suggest a misalignment between instructor perception and estimates of balance challenge at the program level. Further investigations of methods to assess balance challenge are warranted.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.151
GPT teacher head0.458
Teacher spread0.307 · 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 designObservational
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

Citations0
Published2025
Admission routes3
Has abstractyes

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