Rural emergency care 360°: mobilising healthcare professionals, decision-makers, patients and citizens to improve rural emergency care in the province of Quebec, Canada: a qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: Emergency departments (EDs) are an important safety net for rural populations. Results of our earlier studies suggest that rural Canadian hospitals have limited access to advanced imaging services and intensive care units and that patients are transferred over large distances. They also revealed significant geographical variations in rural services. In the absence of national standards, our studies raise questions about inequities in rural access to emergency services and the risks for citizens. Our goal is to build recommendations for improving services by mobilising stakeholders interested in rural emergency care. With help and full engagement of stakeholders, we will (1) identify solutions for improving quality and performance in rural EDs; (2) formulate and prioritise recommendations; (3) transfer knowledge of the recommendations to rural EDs and support operationalisation and (4) assess knowledge transfer and explore further impacts of this participatory action research project. METHODOLOGY: We will use a participatory action research approach. We will plan for a governance structure that includes all stakeholders’ representatives, so throughout this project, stakeholders are fully engaged at every step. Our sample will be 26 EDs in rural Quebec. We will conduct semistructured individual and focus group interviews with relevant and representative participants, including patients and citizens (estimated n=200). Interviews will be thematically analysed to extract potential solutions and other qualitative information.An expert panel (±15) will use an analysis grid to develop consensus recommendations from solutions suggested and will evaluate feasibility, impacts, costs, conditions for implementation and establish monitoring indicators. Recommendations will be transferred to stakeholders using tailored knowledge translation strategies (web platform, meetings and so on). DISCUSSION AND EXPECTED RESULTS: This study will result in a comprehensive consensus list of feasible and high-priority recommendations enabling decision-makers in emergency care to implement improvements in rural emergency care in Quebec. ETHICS AND DISSEMINATION: This protocol has been approved by the CSSS Alphonse-Desjardins research ethics committee (Project number: MP 2017-009). The qualitative material will be kept confidential and the data will be presented in a way that respects confidentiality. The dissemination plan for the study includes publications in scientific and professional journals. We will also use social media to disseminate our findings and activities such as communications in public conferences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".