Use of healthcare information and advice among non-urgent patients visiting emergency department or primary care
Bibliographic record
Abstract
BACKGROUND: Healthcare information provided by telephone service and internet sources is growing but has not been shown to reduce inappropriate emergency department (ED) visits. OBJECTIVE: To describe the use of advice or healthcare information among patients with non-urgent illnesses seeking care before attendance at an ED, or primary care (PC) centres in an urban region in Sweden. DESIGN: Patients with non-urgent illnesses seeking care at an ED or patients attending the PC were followed up with a combination of patient interviews, a questionnaire to the treating physician and a prospective follow-up of healthcare use through a population-based registry. RESULTS: Half of the non-urgent patients attending the ED had used healthcare information or advice before the visit, mainly from a healthcare professional source. In PC, men were more likely to have used information or advice compared with women (OR 2.5 95% CI 1.3 to 5.0), whereas the situation was reversed among ED patients (OR=0.4 95% CI 0.2 to 0.9). Men with no previous healthcare experience attending the ED had the lowest use of healthcare information (p<0.01). Very few in both groups had utilised healthcare information on the internet in a case of perceived emergency. CONCLUSION: ED patients rated as non-urgent by the triage nurse used more advice and healthcare information than PC patients, irrespective of the physician-rated urgency of the symptoms. The problem seems not to be lack of information about appropriate ED use, but to find ways to direct the information to the right target group.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".