Determinants of Emergency Department Visits by Older Adults: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic review of the literature on the determinants of hospital emergency department (ED) visits by elders, using a modification of the Andersen behavioral model of health services, adapted to explain ED utilization. METHODS: Relevant articles were identified through MEDLINE and a search of reference lists and personal files. Studies of populations aged 65 or older in which ED visits were a study outcome were included if they were: original, not restricted to a particular medical condition, written in English or French, and investigated one or more determinants. Data were abstracted and checked by two authors using a standard protocol. RESULTS: Fourteen studies (reported in 15 articles) were reviewed, 10 community-based and four using clinical samples. Among ten studies that measured multiple determinants, determinants reported from multivariate analyses included measures of need (perceived and evaluated health status, prior utilization), predisposing factors (health beliefs and sociodemographic variables), and enabling factors (physician availability, regular source of care, family resources, geographical access to services). CONCLUSIONS: Need is usually the primary determinant of ED visits in older people. Controlling for need, predisposing and enabling factors that promote access to primary medical care are associated with reduced ED utilization.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".