Survey of World Federation of Societies of Anaesthesiologists Fellowship Graduates: Applying a Theory-Driven Framework to Assess Training Outcomes
Bibliographic record
Abstract
BACKGROUND: For nearly 30 years, the World Federation of Societies of Anaesthesiologists (WFSA) has supported fellowship programs to develop subspecialty anesthesia leaders from low- and middle-income countries (LMICs). To date, no formal program evaluation has assessed the educational effectiveness, accountability, or impact of such interventions. This study is part of a mixed-methods evaluation and aimed to survey graduates from all WFSA-supported fellowship programs about program processes and consequences. METHODS: This survey is the second phase of an exploratory sequential mixed-methods study. All graduates from WFSA-supported fellowships from 1996 to 2024 were eligible for inclusion. Survey content was informed by Guskey's 5-level evaluation framework for evaluating training programs and findings from a prior qualitative phase. The instrument was pretested and piloted with anesthesiologists not eligible for inclusion and distributed electronically in English, Spanish, and French. RESULTS: We received 264 responses from 388 surveys distributed (response rate of 68.0%). Most respondents completed their fellowship in the past 10 years; fewer graduates were reported between 2020 and 2022 due to the coronavirus disease 2019 (COVID-19) pandemic. Over 90% of respondents reported consistent access to clinical learning, teaching, and mentorship, peer support, and financial support during their fellowships. Fewer than 5% expressed a lack of confidence in their ability to deliver subspecialty care upon returning home. However, nearly 25% reported being unable to provide clinical care to the same standard as during their fellowship, and almost one-third reported insufficient access to essential equipment required for their subspecialty practice. CONCLUSIONS: WFSA-supported fellowship programs were viewed favorably by graduates across all 5 levels of Guskey's framework. The most frequently cited challenge was the transfer of skills and knowledge to home institutions, often due to contextual disparities between well-resourced training centers and under-resourced home environments. These barriers were most pronounced among fellows returning to the most resource-constrained settings. Addressing these barriers-particularly for fellows from the most under-resourced settings should be a priority for further program investment. Despite these limitations, most participants reported contributing to improved clinical service delivery-often beyond their individual practice-supporting the program's goal of developing subspeciality leadership in anesthesiology.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".