Factors associated with people going to the emergency department for non-urgent visits rather than attending a family physician
Bibliographic record
Abstract
Context: Poor access to primary care (PC) has been associated with increased use of emergency departments (ED) for non-urgent reasons. Identifying PC factors associated with non-urgent ED use will inform the development of policies designed to lower this usage. Objective: Determine PC factors associated with non-urgent ED use. Design: 1) Canada-wide, and 2) St. John's, NL ED cross-sectional surveys. Participants: 1) Adult PC patients across Canada 2) adult ED patients at Health Sciences Centre, St. John's, NL. Outcome Measures: Patient attended the ED for non-urgent reasons. Results: Limited availability of after-hours services (OR=2.08,p<0.0001) and the ability to arrange an appointment as soon as wanted (OR=0.56,p<0.0001) were significantly associated with non-urgent ED use within the Canada-wide data. Non-urgent St. John’s ED users report that restricted hours of operation influenced them to attend the ED, more than other users (62.5%vs.25.0%, p=0.0083). Conclusions: Limited hours and timely availability of services affect patients’ decisions to attend the ED for non-urgent issues.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".