Are Canadian General Internal Medicine training program graduates well prepared for their future careers?
Bibliographic record
Abstract
BACKGROUND: At a time of increased need and demand for general internists in Canada, the attractiveness of generalist careers (including general internal medicine, GIM) has been falling as evidenced by the low number of residents choosing this specialty. One hypothesis for the lack of interest in a generalist career is lack of comfort with the skills needed to practice after training, and the mismatch between the tertiary care, inpatient training environment and "real life". This project was designed to determine perceived effectiveness of training for 10 years of graduates of Canadian GIM programs to assist in the development of curriculum and objectives for general internists that will meet the needs of graduates and ultimately society. METHODS: Mailed survey designed to explore perceived importance of training for and preparation for various aspects of Canadian GIM practice. After extensive piloting of the survey, including a pilot survey of two universities to improve the questionnaire, all graduates of the 16 universities over the previous ten years were surveyed. RESULTS: Gaps (difference between importance and preparation) were demonstrated in many of the CanMEDS 2000/2005 competencies. Medical problems of pregnancy, perioperative care, pain management, chronic care, ambulatory care and community GIM rotations were the medical expert areas with the largest gaps. Exposure to procedural skills was perceived to be lacking. Some procedural skills valued as important for current GIM trainees and performed frequently (example ambulatory ECG interpretation) had low preparation ratings by trainees. Other areas of perceived discrepancy between training and practice included: manager role (set up of an office), health advocate (counseling for prevention, for example smoking cessation), and professional (end of life issues, ethics). CONCLUSION: Graduates of Canadian GIM training programs over the last ten years have identified perceived gaps between training and important areas for practice. They have identified competencies that should be emphasized in Canadian GIM programs. Ongoing review of graduate's perceptions of training programs as it applies to their current practice is important to ensure ongoing appropriateness of training programs. This information will be used to strengthen GIM training programs in Canada.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".