Retention and Migration of Rwandan Anesthesiologists: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Health care professional migration continues to challenge countries where the lack of surgical and anesthesia specialists results in being unable to address the global burden of surgical disease in their populations. Medical migration is particularly damaging to health care systems that are just beginning to scale up capacity building of human resources for health. Anesthesiologists are scarce in low-resource settings. Defining reasons why anesthesiologists leave their country of training through in-depth interviews may provide guidance to policy makers and academic organizations on how to retain valuable health professionals. METHODS: There were 24 anesthesiologists eligible to participate in this qualitative interview study, 15 of whom are currently practicing in Rwanda and 9 had left the country. From the eligible group, interviews were conducted with 13 currently practicing in Rwanda and 2 who had left to practice elsewhere. In-depth interviews of approximately 60 minutes were used to define themes influencing retention and migration among anesthesiologists in Rwanda. Interviews were conducted using a semistructured guide and continued until theoretical sufficiency was reached. Thematic analysis was done by 4 members of the research team using open coding to inductively identify themes. RESULTS: Interpretation of results used the framework categorizing themes into push, pull, stick, and stay to describe factors that influence migration, or the potential for migration, of anesthesiologists in Rwanda. While adequate salary is essential to retention of anesthesiologists in Rwanda, other factors such as lack of equipment and medication for safe anesthesia, isolation, and demoralization are strong push factors. Conversely, a rich academic life and optimism for the future encourage anesthesiologists to stay. CONCLUSIONS: Our study suggests that better clinical resources and equipment, a more supportive community of practice, and advocacy by mentors and academic partners could encourage more staff anesthesiologists to stay and work in Rwanda.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".