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Record W4396696196 · doi:10.5336/healthsci.2023-99239

Investigation of User Satisfaction and Associated Factors in Geriatrics Using Walking Aids: A Cross Sectional Study

2024· article· en· W4396696196 on OpenAlexaboutno aff
Yasin YURT, Ender Ayvat

Bibliographic record

VenueTurkiye Klinikleri Journal of Health Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyGeriatricsPsychologyMedicineGerontologyPsychiatry

Abstract

fetched live from OpenAlex

Objective: We aimed to investigate user satisfaction and associated factors in geriatrics using a walking aid. Material and Methods: The Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 was used to assess the satisfaction of 269 individuals aged ≥65 years using any walking aid. The relationships between satisfaction and age, years of use, body mass index, number of falls in the last year, physical activity level and health-related quality of life were analyzed. Results: The most commonly used walking aid was cane (78.8%). Ease of use was the most satisfied feature, while adjustments was the least satisfied feature. The three most important features were safety, ease of use and weight. Walking aid satisfaction had weak negative correlations with physical activity (r=-0.246) and quality of life (r=-0.131) (p<0.05). In addition, 41.6% of the participants stated that they had fallen at least once in the last year and 70.7% of them did not use a walking aid during the fall. Conclusion: Geriatrics with lower quality of life and physical activity values tend to have higher user satisfaction with walking aids with a weak relationship. Satisfaction results may contribute to the design and selection of appropriate walking aids for this population.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.433
Teacher spread0.315 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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