Assessment of rural emergency department physician staff, hiring practices and needs
Bibliographic record
Abstract
INTRODUCTION: Rural communities suffer from an unequal access to health-care resources. The purpose of this study was to characterise Emergency Departments (EDs) in the Champlain Local Health Integration Network (LHIN) and determine their barriers to recruitment and retention of emergency physicians. METHODS: A survey was sent to the 17 ED chiefs in the Champlain LHIN area by E-mail through May to December 2019. Results were analyzed for common themes and trends. RESULTS: Seven of the 17 hospitals responded to the survey. The average number of physicians staffing the ED was 16, with the majority being Canadian College of Family Physicians certified without additional emergency training. Common described barriers to recruitment include lack of incentives for physicians to work in rural communities, lack of available resources at rural centres, such as specialists and poor flexibility in terms of shift coverage. Barriers to retention included limited incentives to remain in rural communities. CONCLUSION: This study analyzed the demographics and barriers to recruitment and retention in rural EDs. These results can be used to help build strategies that encourage physicians to practise in rural EDs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".