User Perceptions of Safety Flooring Measured Using Multiple Settings and Stakeholders
Bibliographic record
Abstract
Successful implementation of fall-related injury prevention interventions such as safety flooring (SF) requires considering stakeholder feedback. This study investigated user perceptions of a SF product during initial prototype trialing and after implementation within an older adult retirement facility. Sixty participants observed the SF installed in a laboratory (Phase 1) or retirement suite (Phase 2) setting and completed a user experience questionnaire that gathered ratings and comments related to the SF’s effects on residents, employees, and novelty/attractiveness. The SF was positively perceived in both settings and participants supported SF as a valuable intervention that may mitigate fall injury severity, fear of falls, and fall-related disabilities, and improve quality of life. However, participants were uncertain about the SF’s potential effectiveness compared to other injury prevention strategies. Potential challenges included balance issues due to the ramped transitions at the suite entrances and increased acoustic levels on the SF. Participants indicated the disadvantages were limited in comparison to the potential for fall-related injury reduction. This study used a novel and iterative evaluation and engagement process as part of an intervention/implementation process. The findings reaffirm previous outcomes related to SF, while presenting some potential design and implementation issues that may assist in future intervention efforts.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".