Enhancing formative feedback in orthopaedic training: Development and implementation of a competency-based assessment framework
Bibliographic record
Abstract
Objective: The purpose of this study was to develop, implement, and evaluate the effectiveness of an assessment framework aimed at improving formative feedback practices in a Canadian orthopaedic postgraduate training program. Methods: Tool development began in 2014 and took place in 4 phases, each building upon the previous and informing the next. The reliability, validity, and educational impact of the tools were assessed on an ongoing basis, and changes were made accordingly. Results: One hundred eighty-two tools were completed and analyzed during the study period. Quantitative results suggested moderate to excellent agreement between raters (intraclass correlation coefficient = 0.54-0.93), and an ability of the tools to discriminate between learners at different stages of training (p’s < 0.05). Qualitative data suggested that the tools improved both the quality and quantity of formative feedback given by assessors and had begun to foster a culture change around assessment in the program. Conclusions: The tool development, implementation, and evaluation processes detailed in this article can serve as a model for other training programs to consider as they move towards adopting competency-based approaches and refining current assessment practices.
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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 | 0.113 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".