Protocol for the SAFEST review: the Shock-Absorbing Flooring Effectiveness SysTematic review including older adults and staff in hospitals and care homes
Bibliographic record
Abstract
INTRODUCTION: Falls in hospitals and care homes are a major issue of international concern. Inpatient falls are the most commonly reported safety incident in the UK's National Health Service (NHS), costing the NHS £630 million a year. Injurious falls are particularly life-limiting and costly. There is a growing body of evidence on shock-absorbing flooring for fall-related injury prevention; however, no systematic review exists to inform practice. METHODS AND ANALYSIS: We will systematically identify, appraise and summarise studies investigating the clinical and cost-effectiveness, and experiences of shock-absorbing flooring in hospitals and care homes. Our search will build on an extensive search conducted by a scoping review (inception to May 2016). We will search electronic databases (AgeLine, CINAHL, MEDLINE, NHS Economic Evaluation Database, Scopus and Web of Science; May 2016-present), trial registries and grey literature. We will conduct backward and forward citation searches of included studies, and liaise with study researchers. We will evaluate the influence of floors on fall-related injuries, falls and staff work-related injuries through randomised and non-randomised studies, consider economic and qualitative evidence, and implementation factors. We will consider risk of bias, assess heterogeneity and explore potential effect modifiers via subgroup analyses and sensitivity analyses. Where appropriate we will combine studies through meta-analysis. We will use the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach to evaluate the quality of evidence and present the results using summary of findings tables, and adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. ETHICS AND DISSEMINATION: We will follow the ethical principles of systematic review conduct, by attending to publication ethics, transparency and rigour. Our dissemination plan includes peer-reviewed publication, presentations, press release, stakeholder symposium, patient video and targeted knowledge-to-action reports. This review will inform decision-making around falls management in care settings and identify important directions for future research. PROSPERO REGISTRATION NUMBER: CRD42019118834.
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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 | 0.098 | 0.211 |
| Meta-epidemiology (narrow) | 0.014 | 0.015 |
| Meta-epidemiology (broad) | 0.048 | 0.025 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.164 | 0.038 |
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".