Lessons learned in allergy and immunology training: a survey analysis
Bibliographic record
Abstract
BACKGROUND: There is currently little Canadian data to assess how well traditional time-based residency training programs have prepared residents for careers in Clinical Immunology and Allergy (CIA). This study aims to identify the perceived preparedness of residents in various areas of practice upon the completion of a Canadian CIA residency training program. METHODS: In the summer of 2020, an electronic survey was sent to 2018 and 2019 graduates of Canadian CIA training programs by the Canadian Society of Allergy and Clinical Immunology (CSACI). RESULTS: Former residents felt well prepared in most Medical Expert areas. Residents felt less prepared for the intrinsic roles of Leader, Communicator, Collaborator, Health Advocate, Scholar, and Professional. The majority of the intrinsic competencies were learned through mentorship and on the job after finishing training. CONCLUSIONS: Upon completion of training, Canadian CIA residents felt well prepared for many competencies, particularly in Medical Expert areas. Training programs may wish to focus on various intrinsic competencies in order to better prepare residents for transition to practice. Academic half-day was not identified as a primary learning centre for intrinsic competencies, suggesting that new teaching strategies may be required.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".