Portrait of rural emergency departments in Québec and utilization of the provincial emergency department management Guide: cross sectional survey
Bibliographic record
Abstract
BACKGROUND: Rural emergency departments (EDs) constitute crucial safety nets for the 20% of Canadians who live in rural areas. Pilot data suggests that the province of Québec appears to provide more comprehensive access to services than do other provinces. A difference that may be attributable to provincial policy/guidelines "the provincial ED management Guide". The aim of this study was to provide a detailed description of rural EDs in Québec and utilization of the provincial ED management Guide. METHODS: We selected EDs offering 24/7 medical coverage, with hospitalization beds, located in rural or small towns. We collected data via telephone, paper, and online surveys with rural ED/hospital staff. Data were also collected from Québec's Ministry of Health databases and from Statistics Canada. We computed descriptive statistics, ANOVA and t-tests were used to examine the relationship between ED census, services and inter-facility transfer requirements. RESULTS: A total of 23 of Québec's 26 rural EDs (88%) consented to participate in the study. The mean annual ED visits was 18 813 (Standard Deviation = 6 151). Thirty one percent of ED physicians were recent graduates with fewer than 5 years of experience. Only 6 % had residency training or certification in emergency medicine. Teams have good local access (24/7) to diagnostic equipment such as CT scanner (74%), intensive unit care (78%) and general surgical services (78%), but limited access to other consultants. Sixty one percent of participants have reported good knowledge of the provincial ED management Guide, but only 23% of them have used the guidelines. Furthermore, more than 40% of EDs were more than 300 km from levels 1 to 2 trauma centers, and only 30% had air transport access. CONCLUSIONS: Rural EDs in Québec are staffed by relatively new graduates working as solo physicians in well-resourced and moderately busy (by rural standards) EDs. The provincial ED management Guide may have contributed to this model of service attribution. However, the majority of rural ED staff report limited knowledge or use of the provincial ED management Guide and increased efforts at disseminating this Guide are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".