An Evaluation of the Transfer of Skills and Knowledge from Two World Federation of Societies of Anaesthesiologists Fellowship Programs
Bibliographic record
Abstract
BACKGROUND: Subspecialist training is an important part of developing human resources for health and for some learners, may require taking place in another, higher-resourced country. Despite effective learning of skills and knowledge in a different, more highly resourced context, transfer of these skills and knowledge back to a more poorly resourced context can be a challenge. We aimed to evaluate the transfer of skills and knowledge in 2 World Federation of Societies of Anaesthesiologists (WFSA) fellowship programs. METHODS: This qualitative program evaluation study, guided by Guskey's evaluation framework, used in-depth interviews of both faculty and graduates of the 2 fellowship programs. Interviews were conducted remotely, transcribed verbatim, and analyzed using qualitative content and pattern analysis. RESULTS: We interviewed 2 administrators, 10 faculty members, 17 graduated fellows, and 3 graduated fellows now in the role of faculty member in that fellowship. Key themes were barriers and enablers to the transfer of skills, including workplace and staffing, resources, mentorship, the interprofessional team, and leadership. Graduated fellows were able to have an impact on returning home in the areas of practice and service development, research, and teaching. CONCLUSIONS: Our study found that the 2 fellowship programs had variable success in the transfer of learned skills and knowledge back to the fellows' "home" institutions. Contextual differences between the fellowship institution and the home institution were the main source of barriers to transfer, and fellows from different countries had diverse needs. Supporting the transfer of knowledge and skills should be an explicit goal of these fellowship programs, and as such, should be considered in the recruitment of fellows, curriculum development, and in how the success of a fellowship is evaluated. Curricula should not just focus on medical knowledge and skills, but also skills in leading change and in education.
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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".