Reasons for Calling EMS
Bibliographic record
Abstract
Background: Frequent callers of emergency medical services comprise a disproportionate percentage of emergency department visits. This study aims to describe reasons for calling 911 and healthcare services among frequent callers in Ontario. Methods: Cross-sectional research design. A mailed self-administered survey was conducted. Participants were residents of an urban City in Canada, 18 years or older, and had called 911 at least 5 times in the past year. Dillman’s Total Design Method was used to implement the survey. Data was summarized using frequency distribution. Subgroup analyses were performed to assess for factors associated with reasons for calling in the past, reasons for calling in the future, and health services utilized. Results: Among the 67 participants, 47.8% were over 65, 50.70% were female, 85.1% were unemployed, and 38.8% lived alone. Most frequently reported reasons for having called 911 were inability to get up after a fall (41.8%), exacerbation of chronic medical conditions (37.3%), inability to get to the hospital (34.3%), experiencing severe pain (34.3%), and anxiety attacks (23.9%). Subgroup analysis showed significant associations: unemployment was associated with calling due to an alcohol or drug overdose (OR=6.50; CI: 1.16, 36.26); age over 65 had a lower odds of calling in the future for alcohol or drug overdose (OR=0.06; CI: 0.01, 0.51), for serious allergic response (OR=0.22; CI:0.081, 0.740) or for severe pain (OR=0.18; CI: 0.064, 0.518); age over 65 (OR=4.46CI=1.24-17.41) had higher odds of using Community Care Access Centers; and male participants had lower odds of using telehealth (OR=0.19; CI:0.038, 0.97). Conclusions: Policy makers should review these reasons for frequently calling 911 and implement a system to help frequent callers use more appropriate health care services including a non-ambulance response to calls related to falls, transport to medical care facilities, or anxiety attacks. This might free up ambulance services for more urgent calls.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".