Community emergency medical services approaches to fall prevention: a systematic review
Bibliographic record
Abstract
BACKGROUND: Falls are a leading cause of morbidity and mortality among older adults in the USA. Current approaches to fall prevention often rely on referral by primary care providers or enrolment during inpatient admissions. Community emergency medical services (CEMS) present a unique opportunity to rapidly identify older adults at risk for falls and provide fall prevention interventions in the home. In this systematic review, we seek to assess the efficacy and qualitative factors determining success of these programs. METHODS: Studies reporting the outcomes of fall prevention interventions delivered by EMS were identified by searching the electronic databases PubMed, Embase, Web of Science Core Collection, CINAHL and the Cochrane Central Register of Controlled Trials through 11 July 2023. RESULTS: 35 studies including randomised and non-randomised experimental trials, systematic reviews and qualitative research primarily from Western Europe, the USA, Australia and Canada were included in our analysis. Current fall prevention efforts focus heavily on postfall referral of at-risk community members. CEMS fall prevention interventions reduced all-cause and fall-related emergency department encounters, subsequent falls and EMS calls for lift assist. These interventions also improved patient health-related quality of life, independence with activities of daily living, and secondary health outcomes. CONCLUSIONS: CEMS programmes provide an opportunity for direct, proactive fall prevention on the individual level. Addressing barriers to implementation in the context of current emergency medical systems in the USA is the next step toward widespread implementation of these novel fall prevention interventions.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".