Leveraging Innovation to Improve Rural and Remote Emergency Health Services in British Columbia
Bibliographic record
Abstract
Background: Almost twenty percent of BC population live in rural settings; yet accessing healthcare, particularly emergency health services (EHSs), is challenging for rural and remote populations as they continue to experience systemic inequities, higher incidence of chronic illnesses, and lower access to healthcare services. Access to EHSs is essential for integrated health services for these populations. Despite the challenges, healthcare providers have demonstrated resiliency in delivering EHSs in rural and remote communities through innovative approaches such as Real-time Virtual Support (RTVS). Additionally, the BC government has made significant efforts to improve EHSs by increasing the budget for innovation (e.g., virtual care and broadband technology) and emergency transport services, however, there is limited information on these innovations in the literature. This study aims to explore and understand how current innovations emerged and evolved in rural and remote EHSs to identify what works, for whom, and in which contexts, and to develop recommendations for policymakers and decision-makers to leverage innovation in rural and remote EHSs. Approach: The case study methodology, with a focus on narrative inquiry, was employed to study three rural and remote cases: two in northern BC and one in interior BC. Mixed methods were used to collect data over two phases. Phase I involved descriptive data collection and community visits. Phase II involved: semi-structured interviews with policymakers, decision-makers, managers, health providers, and administrative staff (n=3); focus groups with patients and community members (n=4); and aggregate administrative data collection from various relevant organizations (e.g., health authorities). Thematic analysis was conducted to identify common themes in the qualitative data. Quantitative data analysis, using descriptive statistics, is in process. A preliminary report was developed and shared with each case study participants through an in-person follow-up dialogue (n=20) to discuss the results and co-create actions moving EHSs innovations forward. Final individual case study reports and cross-case reports will be shared with the communities. Results: Qualitative data showed that various innovations such as Real Time Virtual Support ( pathways, virtual care, emergency physician online support, mechanical CPR devices, translation apps, and electronic triage and transfer systems were being used to provide EHSs. These innovations, particularly RTVS, equipped health providers with additional support, increased community membersaccess to EHSs, and reduced unnecessary transfer of patients out of the community. However, barriers to innovations such as limited resources (e.g., funding, digital health inequities, innovations for mental health services and support), cross-jurisdictional policies, and staff shortages were highlighted. Recommendations to enhance innovations in EHSs such as increasing funding for rural infrastructures, expanding RTVS services, exploring the alignment of policies, planning proactively, and collaborating with the local government were identified. Quantitative data analysis is currently in progress and will be included in the presentation. Implications: Recent innovations in EHSs have improved access to integrated care in rural and remote communities. Results of this study will guide policymakers and decision-makers to advance, adapt, and scale innovation and facilitate equitable access to EHSs for rural, remote, and Indigenous communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".