Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".