“The Most Crushing Thing”: Understanding Resident Assessment Burden in a Competency-Based Curriculum
Bibliographic record
Abstract
Background: Competency-based medical education (CBME) was expected to increase the workload of assessment for graduate training programs to support the development of competence. Learning conditions were anticipated to improve through the provision of tailored learning experiences and more frequent, low-stakes assessments. Canada has adopted an approach to CBME called Competence by Design (CBD). However, in the process of implementation, learner anxiety and assessment burden have increased unexpectedly. To mitigate this unintended consequence, we need a stronger understanding of how resident assessment burdens emerge and function. Objective: This study investigates contextual factors leading to assessment burden on residents within the framework of CBD. Methods: Residents were interviewed about their experiences of assessment using constructivist grounded theory. Participants (n=21) were a purposive sample from operative and perioperative training programs, recruited from 6 Canadian medical schools between 2019 and 2020. Self-determination theory was used as a sensitizing concept to categorize findings on types of assessment burden. Results: Nine assessment burdens were identified and organized by threats to psychological needs for autonomy, relatedness, and competence. Burdens included: missed opportunities for self-regulated learning, lack of situational control, comparative assessment, lack of trust, constraints on time and resources, disconnects between teachers and learners, lack of clarity, unrealistic expectations, and limitations of assessment forms for providing meaningful feedback. Conclusions: This study contributes a contextual understanding of how assessment burdens emerged as unmet psychological needs for autonomy, relatedness, and competence, with unintended consequences for learner well-being and intrinsic motivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".