Increased demand for paramedic transports to the emergency department in Ontario, Canada: a population-level descriptive study from 2010 to 2019
Bibliographic record
Abstract
PURPOSE: We examined changes in annual paramedic transport incidence over the ten years prior to COVID-19 in comparison to increases in population growth and emergency department (ED) visitation by walk-in. METHODS: We conducted a population-level cohort study using the National Ambulatory Care Reporting System from January 1, 2010 to December 31, 2019 in Ontario, Canada. We included all patients triaged in the ED who arrived by either paramedic transport or walk-in. We clustered geographical regions using the Local Health Integration Network boundaries. Descriptive statistics, rate ratios (RR), and 95% confidence intervals were calculated to explore population-adjusted changes in transport volumes. RESULTS: Overall incidence of paramedic transports increased by 38.3% (n = 264,134), exceeding population growth fourfold (9.4%) and walk-in ED visitation threefold (13.4%). Population-adjusted transport rates increased by 26.2% (rate ratio 1.26, 95% CI 1.26-1.27) compared to 3.4% for ED visit by walk-in (rate ratio 1.03, 95% CI 1.03-1.04). Patient and visit characteristics remained consistent (age, gender, triage acuity, number of comorbidities, ED disposition, 30-day repeat ED visits) across the years of study. The majority of transports in 2019 had non-emergent triage scores (60.0%) and were discharged home directly from the ED (63.7%). The largest users were persons aged 65 or greater (43.7%). The majority of transports occurred in urbanized regions, though rural and northern regions experienced similar paramedic transport growth rates. CONCLUSION: There was a substantial increase in the demand for paramedic transportation. Growth in paramedic demand outpaced population growth markedly and may continue to surge alongside population aging. Increases in the rate of paramedic transports per population were not bound to urbanized regions, but were province-wide. Our findings indicate a mounting need to develop innovative solutions to meet the increased demand on paramedic services and to implement long-term strategies across provincial paramedic systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".