Barriers and facilitators to implementing a regional anesthesia service in a low-income country: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Regional anesthesia is a safe alternative to general anesthesia. Despite benefits for perioperative morbidity and mortality, this technique is underutilized in low-resource settings. In response to an identified need, a regional anesthesia service was established at the University Teaching Hospital of Kigali (CHUK), Rwanda. This qualitative study investigates the factors influencing implementation of this service in a low-resource tertiary-level teaching hospital. METHODS: Following service establishment, we recruited 18 local staff at CHUK for in-depth interviews informed by the Consolidated Framework for Implementation Research (CFIR). Data were coded using an inductive approach to discover emergent themes. RESULTS: Four themes emerged during data analysis. Patient experience and outcomes: Where equipment failure is frequent and medications unavailable, regional anesthesia offered clear advantages including avoidance of airway intervention, improved analgesia and recovery, and cost-effective care. Professional satisfaction: Morale among healthcare providers suffers when outcomes are poor. Participants were motivated to learn techniques that they believe improve patient care. Human and material shortages: Clinical services are challenged by high workload and human resource shortages. Advocacy is required to solve procurement issues for regional anesthesia equipment. Local engagement for sustainability: Participants emphasized the need for a locally run, sustainable service. This requires broad engagement through education of staff and long-term strategic planning to expand regional anesthesia in Rwanda. CONCLUSION: While the establishment of regional anesthesia in Rwanda is challenged by human and resource shortages, collaboration with local stakeholders in an academic institution is pivotal to sustainability.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".