Sustaining Rural Access to Emergency Care through Collaborative Emergency Centres in Nova Scotia
Bibliographic record
Abstract
Collaborative Emergency Centres (CECs) were introduced in Nova Scotia in 2011 to address gaps in rural access to emergency care through enhanced primary care, urgent care, and interprofessional teams supported by remote physicians. In the wake of a historic election win by the New Democratic Party (NDP) in 2009, who promised to keep rural emergency departments open, a series of reports highlighted the troubles with small hospital emergency departments and suggested the development of CECs as a novel model of care. CECs aimed to improve access to emergency care in rural areas by matching the offered services to the needs of the community. The policy window for this reform was created through the convergence of a publicly recognized crisis in rural emergency department closures, a nationwide trend toward community-centred and interprofessional care models, and a historic NDP provincial government victory. Budgetary allocations and enabling legislation supported the development of the first set of four CECs for the province. Ministerial emergency department accountability reports and the Care Right Now report proclaimed the success of the CECs in reducing the number of hours of unplanned emergency department closures and in increasing rural communities' access to primary and emergency care. The CECs allowed Nova Scotia to provide access to around-the-clock emergency care at a greatly reduced cost, improved the work-life balance for rural physicians, and created a case for successful implementation of interprofessional teams in other environments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".