Bibliographic record
Abstract
Ensuring that there are enough physicians to staff the emergency department (ED) is a major issue for hospital managers.According to a recent ICES Practice Atlas on ED Services in Ontario (Chan et al.2001), this task may become increasingly difficult.From fiscal year 1993 to 2000 , the number of physicians working in EDs declined by 21%, from 2,525 to 1,987.One reason fewer physicians are practicing emergency medicine is because the ED is an increasingly taxing work environment.Although the per capita use of EDs has declined by 10% in the past seven years, this decline was attributable to lower ED use by children, who tend to be lower acuity cases (e.g.colds and ear infections).On the other hand, the per capita use of ED services by the elderly is rising and they present with much more complex conditions.Furthermore, 19 hospitals in Ontario out of 201 closed during the study period.As a result, ED visit volumes at the remaining hospitals rose by 10%, from 19,100 to 21,000 per year.Another warning sign is the aging ED physician workforce.In 1993, 40% of ED physicians were under the age of 40.By 2000, this proportion dropped to 24%.This phenomenon may be related to policies in the 1990s that were implemented to restrict the growth in the supply of physicians (Barer et al. 1996).These policies were targeted, perhaps unfairly, at young physicians.A third issue concerns the existence of highly predictable peaks in ED volume.EDs see 9% more patients per day on weekends than average.The week between Christmas and New Year's is the busiest of the year, and volumes increase by up to one-third above the volumes seen on other public holidays.This raises the question of how to ensure sufficient staffing on these days.Three questions for hospital managers to consider, in light of these findings, are as follows:
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".