Portrait of rural emergency departments in Quebec and utilisation of the Quebec Emergency Department Management Guide: a study protocol
Bibliographic record
Abstract
INTRODUCTION: Emergency departments are important safety nets for people who live in rural areas. Moreover, a serious problem in access to healthcare services has emerged in these regions. The challenges of providing access to quality rural emergency care include recruitment and retention issues, lack of advanced imagery technology, lack of specialist support and the heavy reliance on ambulance transport over great distances. The Quebec Ministry of Health and Social Services published a new version of the Emergency Department Management Guide, a document designed to improve the emergency department management and to humanise emergency department care and services. In particular, the Guide recommends solutions to problems that plague rural emergency departments. Unfortunately, no studies have evaluated the implementation of the proposed recommendations. METHODS AND ANALYSIS: To develop a comprehensive portrait of all rural emergency departments in Quebec, data will be gathered from databases at the Quebec Ministry of Health and Social Services, the Quebec Trauma Registry and from emergency departments and ambulance services managers. Statistics Canada data will be used to describe populations and rural regions. To evaluate the use of the 2006 Emergency Department Management Guide and the implementation of its various recommendations, an online survey and a phone interview will be administered to emergency department managers. Two online surveys will evaluate quality of work life among physicians and nurses working at rural emergency departments. Quality-of-care indicators will be collected from databases and patient medical files. Data will be analysed using statistical (descriptive and inferential) procedures. ETHICS AND DISSEMINATION: This protocol has been approved by the CSSS Alphonse-Desjardins research ethics committee (Project MP-HDL-1213-011). The results will be published in peer-reviewed scientific journals and presented at one or more scientific conferences.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".