Development of a stick-on hip protector: A multiple methods study to improve hip protector design for older adults in the acute care environment
Bibliographic record
Abstract
INTRODUCTION: Over 90% of hip fractures in older adults result from falls, and hospital patients are at especially high risk. Specific types of wearable hip protectors have been shown to reduce hip fracture risk during a fall by up to 80%, but user compliance has averaged less than 50%. We describe the development and evaluation of a "stick-on" hip protector (secured over the hip with a skin-friendly adhesive) for older patients in acute care. METHODS: An initial version of the product was evaluated with six female patients (aged 76-91) in a hospital ward, who were asked to wear it for one week. We subsequently refined the product through biomechanical testing and solicited feedback from 43 health professionals on a second prototype. RESULTS: The first prototype was worn by five of six patients for the full week or duration of their hospital stay. The second prototype (20 mm thick, surface area 19 × 15.5 cm) provided 36% force attenuation, more than common garment-based models (20-21%). Feedback from patients and health professionals highlighted usability, comfort, cost, and appearance. CONCLUSIONS: Our results from biomechanical and user testing support the need for further work to determine the value of stick-on hip protectors in acute care.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".