Validating objectives and training in Canadian paediatrics residency training programmes
Bibliographic record
Abstract
BACKGROUND: Changing health care systems and learning environments with reduction in resident work hours raises the question: "Are we adequately training our paediatricians?" AIMS: (1) Identify clinical competencies to be acquired during paediatric residency training to enable graduates to practise as consultant paediatricians; (2) Identify gaps in preparedness during training and; (3) Review and validate competencies contained in the Royal College of Physicians and Surgeons of Canada (RCPSC) objectives of training (OTR) for paediatrics. METHODS: A questionnaire with 19 classification domains containing 92 clinical competencies was administered to RCPSC certified paediatricians who completed residency training in Canada from June 2004 to June 2008. For each competency, paediatricians were asked to indicate the importance and their degree of preparedness upon entering practice. Gap scores (GSs) between importance and preparedness were calculated. RESULTS: Response rate was 43% (187/435); 91.3% (84/92) of competencies in the RCPSC OTR were identified as important. Paediatricians felt less than adequately prepared for 25% (23/92) of competencies; 40 competencies had GSs >10%. CONCLUSIONS: The unique approach used in this study is useful in validating OTR as well as the preparation of residents in relation to OTR. The results indicate a potential need for additional training in specific competencies.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".