Intended and Unintended Consequences: The Impact of Competence by Design Implementation on the Experiences of Trainees in a Pediatric Residency Program
Bibliographic record
Abstract
Background: Recent reports from Canadian academic institutions provide insights into the resident experience following Competence by Design (CBD) implementation and have suggested important unintended consequences, such as increased administrative burden. Since pediatric residency programs transitioned in July 2021, half of McMaster University’s Pediatric Residency Program residents were in competency-based medical education (CBME) streams in the 2022-2023 academic year, and half were in non-CBME streams. As a result, our objective was to compare the residents' experiences in these two streams for observation, feedback, assessment, and well-being and burnout. Methods: We studied resident physicians in the McMaster Pediatric Residency Program (n = 37 eligible residents), employing a two-phased quantitative sequential exploratory approach. In Phase 1, residents used electronic journals (e-journals) to log feedback, observation, and assessments over two weeks and time spent on clinical assessments. Phase 2 involved an anonymous survey to recall experiences with assessment, supervision, and observation. Validated single-item measures from the Maslach Burnout Inventory assessed emotional exhaustion and depersonalization. Results: The e-journal response rate was 56.8% (21/37), with complete responses from 10 CBME and 11 non-CBME residents. Our analyses indicated that CBME residents attempted more assessments (p < .01) and spent more time on them (126 minutes vs. 28 minutes for non-CBME residents, p < .01). Both groups reported similar rates of observation and feedback. The survey response rate was 59.5% (22/37), with complete responses from 15 CBME and seven non-CBME residents. Burnout was prevalent, with CBME residents more likely to endorse assessments as a cause of feeling burnt out (p = 0.023). Discussion: Given the significantly increased contribution of clinical assessments to burnout in the CBME cohorts, it is crucial to further investigate this curriculum’s administrative burden and its implications for resident well-being. Further research is needed to explore the changes to the quality and quantity of feedback, observation, and assessment in CBME.
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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".