Reducing fall risk for home care workers with slip resistant winter footwear
Bibliographic record
Abstract
Falls on icy surfaces are the leading cause of occupational injuries for workers exposed to outdoor winter conditions. Slip resistant footwear has been shown to reduce the risk of falls for indoor workers but until recently, there was no accepted standard for evaluating the slip resistance of winter footwear on icy surfaces. Our team recently developed a lab-based testing protocol for measuring footwear slip resistance. This protocol, called the Maximum Achievable Angle (MAA) test, measures the steepest ice-covered slope that participants can walk up and down without experiencing a slip in a simulated winter environment. This lab-based protocol has found there is wide variability in the performance of commercially available winter footwear. In particular, we have found that a new generation of footwear that incorporates composite materials in the outsole, performs much better than most other footwear. The objective of this project was to investigate whether the footwear that performed well in our lab-based testing would reduce the risk of slips and/or falls in real-world winter conditions. One hundred and ten home healthcare workers from SE Health were recruited for this study and were asked to report their exposure to icy surfaces along with the numbers of slips and numbers of falls they experienced each week using online surveys over eight weeks in the winter. Fifty participants (the intervention group) were provided winter footwear that were among the best performing in the MAA test. The remaining sixty participants (the control group) wore their own footwear for the duration of the study. A total of 563 slips and 36 falls were reported over the eight-week data collection period. The intervention group consistently reported fewer slips (127 vs 436) and fewer falls (6 vs 30) compared to the control group. We found the slip rate in the intervention group was between 68.0% and 68.7% lower than the control group. Similarly, the fall rate was between 78.5% and 81.5% lower in the intervention group compared to the control group. These findings demonstrate that footwear that performs well in the MAA test can reduce the risk of both slips and falls in real-world winter conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".