Portrait of trauma care in Quebec's rural emergency departments and identification of priority intervention needs to improve the quality of care: a study protocol
Bibliographic record
Abstract
INTRODUCTION: Trauma remains the primary cause of death in individuals under 40 years of age in Canada. In Quebec, the Trauma Care Continuum (TCC) has been demonstrated to be effective in decreasing the mortality rate among trauma victims. Although rural citizens are at greater risk for trauma and trauma death, no empirical data concerning the effectiveness of the TCC for the rural population in Quebec are available. The emergency departments (EDs) are important safety nets for rural citizens. However, our data indicate that access to diagnostic support services, such as intensive care units and CT is limited in rural areas. The objectives are to (1) draw a portrait of trauma services in rural EDs; (2) explore geographical variations in trauma care in Quebec; (3) identify adaptable factors that could reduce variation; and (4) establish consensus solutions for improving the quality of care. METHODS AND ANALYSIS: The study will take place from November 2015 to November 2018. A mixed methodology (qualitative and quantitative) will be used. We will include data (2009-2013) from all trauma victims treated in the 26 rural EDs and tertiary/secondary care centres in Quebec. To meet objectives 1 and 2, data will be gathered from the Ministry's Database of the Quebec Trauma Registry Information System. For objectives 3 and 4, the project will use the Delphi method to develop consensus solutions for improving the quality of trauma care in rural areas. Data will be analysed using a Poisson regression to compare mortality rate during hospital stay or death on ED arrival (objectives 1 and 2). Average scores and 95% CI will be calculated for the Delphi questionnaire (objectives 3 and 4). ETHICS AND DISSEMINATION: This protocol has been approved by CSSS Alphonse-Desjardins research ethics committee (Project MP-HDL-2016-003). The results will be published in peer-reviewed journals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".