Defining fitness for purpose in South African anaesthesiologists using a Delphi technique to assess the CanMEDS framework
Bibliographic record
Abstract
Background: Training of South African anaesthesiologists is based on the Canadian Medical Education Directives for Specialists (CanMEDS). However, the applicability of CanMEDS in this context has not been assessed. An expert panel participated in a Delphi process to create an appropriate expanded list of CanMEDS competencies that may be used in the future to assess fitness for purpose of local graduates. Methods: This descriptive study comprised a representative panel of 16 experts surveyed electronically over three rounds to assess the importance of the existing CanMEDS roles and enabling competencies and suggested additions deemed applicable locally. The primary outcome was the creation of a list of competencies applicable to South Africa. Results: There was a 100% response rate for all three rounds. Based on the existing seven CanMEDS meta-competencies (Medical Expert, Collaborator, Communicator, Leader, Scholar, Professional and Health Advocate), respondents scored the importance of 89 enabling competencies and 19 additional competencies. Seven CanMEDS enabling competencies did not achieve consensus and were excluded. Nineteen new enabling competencies and two new meta-competencies (Humaneness, Context Awareness) achieved consensus and were added. Median ratings of importance of meta-competencies showed highest scores for Medical Expert and Collaborator and lowest scores for Health Advocate. Weighting of meta-competencies revealed highest scores for Medical Expert and Professional with all others equally weighted. Conclusion: This study has formulated an adapted CanMEDS list of enabling competencies with the addition of the two new metacompetencies of Context Awareness and Humaneness for use in South African anaesthesiology. This provides a means with which future graduates may be assessed for fitness for purpose.
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 | 0.074 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".