Factors associated with frequent use of emergency-department services in a geriatric population: a systematic review
Bibliographic record
Abstract
BACKGROUND: Frequent geriatric users of emergency departments (EDs) constitute a small group of individuals accounting for a disproportionately high number of ED visits. In addition to overcrowding, this situation might result in a less appropriate response to health needs and negative health impacts. Geriatric patients turn to EDs for a variety of reasons. A better understanding of the variables associated with frequent ED use will help implement interventions best suited for their needs. OBJECTIVE: This review aimed at identifying variables associated with frequent ED use by older adults. METHODS: For this systematic review, we searched Medline, CINAHL, Healthstar, and PsyINFO (before June 2018). Articles written in English or French meeting these criteria were included: targeting a population aged 65 years or older, reporting on frequent ED use, using an observational study design and multivariate regression analysis. The search was supplemented by manually examining the reference lists of relevant studies. Independent reviewers identified articles for inclusion, extracted data, and assessed quality with the JBI Critical Appraisal Checklist for Studies Reporting Prevalence. A narrative synthesis was done to combine the study results. A sensitivity analysis was performed to evaluate the effect of removing the studies not meeting the quality criteria. RESULTS: Out of 5096 references, 8 met our inclusion criteria. A high number of past hospital and ED admissions, living in a rural area adjacent to an urban center, low income, a high number of prescribed drugs, and a history of heart disease were associated with frequent ED use among older adults. In addition, having a principal-care physician and living in a remote rural area were associated with fewer ED visits. Some variables recognized in the literature as influencing ED use among older adults received scant consideration, such as comorbidity, dementia, and considerations related to primary-care and community settings. CONCLUSION: Further studies should bridge the gap in understanding and give a more global portrait by adding important personal variables such as dementia, organizational variables such as use of community and primary care, and contextual variables such as social and economic frailty.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".