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Record W4372291622 · doi:10.1080/26892618.2023.2202662

User Perceptions of Safety Flooring Measured Using Multiple Settings and Stakeholders

2023· article· en· W4372291622 on OpenAlexafffund
Mayank Kalra, Taylor W. Cleworth, Jaimie Killingbeck, Andrew C. Laing

Bibliographic record

VenueJournal of Aging and Environment · 2023
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsResearch Institute for AgingYork UniversityUniversity of Waterloo
FundersAGE-WELLMitacs
KeywordsNoveltyPsychological interventionAttractivenessIntervention (counseling)Applied psychologyPerceptionPoison controlPsychologyHuman factors and ergonomicsOccupational safety and healthStakeholderInjury preventionMedicineMedical emergencyNursingSocial psychology

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.206
Teacher spread0.180 · 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 designSimulation or modeling
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

Citations2
Published2023
Admission routes2
Has abstractyes

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