Personal Characteristics and Experience of Primary Care Predicting Frequent Use of Emergency Department: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: A small number of patients frequently using the emergency department (ED) account for a disproportionate amount of the total ED workload and are considered using this service inappropriately. The aim of this study was to identify prospectively personal characteristics and experience of organizational and relational dimensions of primary care that predict frequent use of ED. METHODS: This study was conducted among parallel cohorts of the general population and primary care patients (N = 1,769). The measures were at baseline (T1), 12 (T2) and 24 months (T3): self-administered questionnaire on current health, health behaviours and primary care experience in the previous year. Use of medical services was confirmed using administrative databases. Mixed effect logistic regression modeling identified characteristics predicting frequent ED utilization. RESULTS: A higher likelihood of frequent ED utilization was predicted by lower socioeconomic status, higher disease burden, lower perceived organizational accessibility, higher number of reported healthcare coordination problems and not having a complete annual check-up, above and beyond adjustment for all independent variables. CONCLUSIONS: Personal characteristics such as low socioeconomic status and high disease burden as well as experience of organizational dimensions of primary care such as low accessibility, high healthcare coordination problems and low comprehensiveness of care are prospectively associated with frequent ED utilization. Interventions developed to prevent inappropriate ED visits, such as case management for example, should tailor low socioeconomic status and patients with high disease burden and should aim to improve experience of primary care regarding accessibility, coordination and comprehensiveness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".