Improving delivery of care in rural emergency departments: a qualitative pilot study mobilizing health professionals, decision-makers and citizens in Baie-Saint-Paul and the Magdalen Islands, Québec, Canada
Bibliographic record
Abstract
BACKGROUND: Emergency departments (EDs) in rural and remote areas face challenges in delivering accessible, high quality and efficient services. The objective of this pilot study was to test the feasibility and relevance of the selected approach and to explore challenges and solutions to improve delivery of care in selected EDs. METHODS: We conducted an exploratory multiple case study in two rural EDs in Québec, Canada. A survey filled out by the head nurse for each ED provided a descriptive statistical portrait. Semi-structured interviews were conducted with ED health professionals, decision-makers and citizens (n = 68) and analyzed inductively and thematically. RESULTS: The two EDs differed with regards to number of annual visits, inter-facility transfers and wait time. Stakeholders stressed the influence of context on ED challenges and solutions, related to: 1) governance and management (e.g. lack of representation, poor efficiency, ill-adapted standards); 2) health services organization (e.g. limited access to primary healthcare and long-term care, challenges with transfers); 3) resources (e.g. lack of infrastructure, limited access to specialists, difficult staff recruitment/retention); 4) and professional practice (e.g. isolation, large scope, maintaining competencies with low case volumes, need for continuing education, teamwork and protocols). There was a general agreement between stakeholder groups. CONCLUSIONS: Our findings show the feasibility and relevance of mobilizing stakeholders to identify context-specific challenges and solutions. It confirms the importance of undertaking a larger study to improve the delivery of care 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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".