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Record W2080821860 · doi:10.7547/1030465

“Good for Older Ladies, Not Me”

2013· article· en· W2080821860 on OpenAlexaff
Annette Davis, Anna Murphy, Terry Haines

Bibliographic record

VenueJournal of the American Podiatric Medical Association · 2013
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedicinePurchasingSelection (genetic algorithm)Telephone surveyFoot (prosody)Qualitative researchGerontologyAdvertisingEngineeringOperations management

Abstract

fetched live from OpenAlex

BACKGROUND: Footwear selection is important among older adults. Little is known about factors that influence footwear selection among older women. If older women are to wear better footwear that reduces their risk of falls and foot abnormalities, then a better understanding of the factors underlying footwear choice is needed. This study aims to identify factors that drive footwear selection and use among older community-dwelling women with no history of falls. METHODS: A cross-sectional survey using a structured, open-ended questionnaire was conducted by telephone interview. The participants were 24 women, 60 to 80 years old, with no history of falls or requirement for gait aids. The responses to open-ended questions were coded and quantified under a qualitative description paradigm. RESULTS: The main themes identified about footwear selection were aesthetics and comfort. Aesthetics was by far the main factor influencing footwear choice. Wearing safe footwear was not identified as a consideration when purchasing footwear. CONCLUSIONS: This study indicates that older women are driven primarily by aesthetics and comfort in their footwear selection. These footwear drivers have implications for health-care providers when delivering fall and foot health education.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.210
Teacher spread0.205 · 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 designNot applicable
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

Citations24
Published2013
Admission routes1
Has abstractyes

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