A summary evidence of prevention measures of falling down for the elderly in care facilities
Bibliographic record
Abstract
ObjectiveTo evaluate and summarize the best available evidences of prevention measures of falling down for the elderly in care facilities.MethodsGuidelines and systematic reviews regarding falling down prevention measures for the elderly in care facilities were collected from Cochrane Library,JBI Library,Registered Nurses′Association of Ontario(RNAO),National Guideline Clearinghouse (NGC),PubMed,EMbase,Scottish Intercollegiate Guideline (SIGN),British Medical Journal Best Practice,BMJ Best Practice,CBM,WanaFang Database and CNKI.The quality evaluation of the literature and data extraction were conducted by three researchers.Then evidence from the literature that met the quality standards was summarized.Results8 articles were enrolled,including 2 guidelines and 6 systematic reviews.There were 24 best practice evidences,including assessment of fall risks,guarantee of a safe environment,encouragement of initiative participation in fall prevention,health education,multifactorial interventions measures,exercise interventions and physical training,medication,dietary interventions,hip protectors,podiatry interventions and educational training.ConclusionIt is suggested that managers and health caregivers of care facilities should take fall preventions for elderly people based on evidences at relevant levels to ensure their safety.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".