Factors Influencing SurgeCon Implementation in Four Canadian Emergency Departments Guided by Consolidated Framework for Implementation Research
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) overcrowding remains a significant national issue in Canada. To address this issue, SurgeCon, a quality improvement program, was implemented to enhance patient flow, improve communication, and reduce wait times. Despite their potential, interventions like SurgeCon lack evidence on real-world implementation and sustainability in high-pressure, resource-limited ED settings. OBJECTIVE: This study explores the factors influencing the implementation of SurgeCon in four Canadian EDs using the Consolidated Framework for Implementation Research (CFIR) to identify facilitators and barriers. METHODS: Data were collected over 2.5 years-before, during, and after SurgeCon implementation-in two rural and two urban EDs in Canada using a longitudinal qualitative research (LQR) design. Forty-two semi-structured interviews with physicians, nurses, and hospital managers were analyzed through inductive and deductive thematic analysis, guided by the CFIR framework. RESULTS: Facilitators were predominantly associated with CFIR's Innovation Characteristics, particularly the perceived benefits of real-time data collection, workflow optimization, and enhanced communication. However, barriers-mainly linked to outer setting (COVID-19 disruptions), inner setting (resource constraints and fragmented communication), and individual characteristics (leadership engagement and motivation)-outweighed these advantages. CONCLUSION: To strengthen adoption, this study proposes eight strategic action plans focusing on leadership commitment, automation, cross-departmental collaboration, feedback loops and change management strategies to maximize facilitators and address implementation barriers. TRIAL REGISTRATION: ClinicalTrials.gov. NCT04789902. 10/03/2021.
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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 | 0.048 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".