Medical residents’ perceptions of their competencies and training needs in health care management: an international comparison
Bibliographic record
Abstract
BACKGROUND: Previous research has shown that Dutch medical residents feel inadequate in certain management areas: 85% had a need for management training and reported preferences on the format of such training. Our objective was to explore if the perceived deficiencies and needs among Dutch residents were similar to those of their peers in other countries, and if a longer duration of the incorporation of the CanMEDS competency framework into curricula as well as management training had an influence on these perceptions. METHODS: Medical residents from Denmark, Canada and Australia were approached for participation. The questionnaire used to survey the perceptions of Dutch residents was slightly modified, translated into English and sent by email to all international participants. RESULTS: Response rates were; Denmark 719/2105 (34%), Canada 177/500 (35%) and Australia 194/1213 (16%) respectively. The Danish as well as the Canadian residents rated their negotiating skills poorly. In Australia the residents found their knowledge on how their specialist department was organized to be insufficient, while residents in the Netherlands rated their knowledge on how the healthcare system was organized as inadequate. In all of the countries, more than 70% of the residents reported a perceived need for management training. CONCLUSIONS: A majority of the residents in all countries felt the need for specific training in developing their management competencies. The adoption of the CanMEDS framework alone seems to be insufficient in meeting residents' perceived educational needs in this area.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".