Preparedness of Residents and Medical Students for the Transition to Competence by Design in Diagnostic Radiology Post-Graduate Medical Education
Bibliographic record
Abstract
Introduction: This needs assessment evaluated residents’ and medical students’ knowledge of Competence by Design (CBD), perceived benefits of and challenges or barriers to the transition to CBD for residents, and perceived overall preparedness for the transition to CBD in diagnostic radiology. Materials and Methods: All diagnostic radiology residents and medical students in Canada were eligible to participate in this national cross-sectional, questionnaire-based needs assessment. Knowledge of CBD was evaluated through participants’ self-reported rating of their knowledge of CBD on a 5-point Likert scale. Perceived benefits of and challenges or barriers to the transition to CBD for residents were rank ordered. Participants’ overall self-reported preparedness for the transition to CBD was assessed on a 5-point Likert scale. Data were summarized by descriptive statistics and bivariate analyses were conducted as appropriate. Results: Ninety-four residents ( n = 77) and medical students ( n = 17) participated in this needs assessment. Participants’ mean ± standard deviation self-reported rating of their overall knowledge of CBD was 2.86 ± .94. Provision of meaningful feedback to learners and learners’ ability to identify their own educational needs were among the highest ranked perceived benefits of the transition to CBD, while demands on time and increased frequency of evaluation were among the highest ranked perceived challenges or barriers to the transition to CBD. Few participants reported being either “prepared” (4.7%) or “somewhat prepared” (14.0%) for the transition to CBD. Conclusion: Preparedness for the transition to CBD in diagnostic radiology may be improved. Targeted interventions to augment the preparedness of residents and medical students should be considered.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".