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Record W2182592185 · doi:10.1590/0104-1169.0331.2659

Connection between competence, usability, environment and risk of falls in elderly adults

2015· article· en· W2182592185 on OpenAlexaffabout
José Alex Leiva-Caro, Bertha Cecilia Salazar-González, Esther C. Gallegos-Cabriales, Marco Vinicio Gómez Meza, Kathleen F. Hunter

Bibliographic record

VenueRevista Latino-Americana de Enfermagem · 2015
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUsabilityDescriptive statisticsPsychologyPoison controlGerontologyLogistic regressionApplied psychologyMedicineEnvironmental healthComputer scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine connections between competence, usability, environment and risk of falls in elderly adults. METHOD: Correlational descriptive study, 123 elderly adults, both male and female, aged 70 years and older were included. Data was collected via the Tinetti Scale, CESD-7 Scale, Montreal Cognitive Assessment, Usability Questionnaire on Housing and Housing Enabler; and sociodemographic and health background certificate data. For data analysis, descriptive and inferential statistics were used, multivariate linear and logistic regression models were adjusted. RESULTS: 42.0% of the elderly adults had presented with falls, with a higher prevalence in women, and in the group of 70-75 years. The physical environment of the house, gait, and usability were set as risk factors for falls. A negative relationship between usability and depressive symptoms, cognitive health, balance, gait, the social and physical environment was found, p <0.05; and a strong positive correlation between walking and balance, p <0.05. CONCLUSION: This study helps to better understand the phenomenon of falling, to find a connection between usability with the risk of falls, and other variables.

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)0.004
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0010.000

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 head0.041
GPT teacher head0.343
Teacher spread0.302 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueRevista Latino-Americana de EnfermagemSame topicBalance, Gait, and Falls PreventionFrench-language works237,207