Using Individual Residents’ Learning Trajectories to Better Understand the Impact of Gaps in Practice
Bibliographic record
Abstract
PURPOSE: To optimize learning, health professional training programs need to achieve the right balance between depth of practice (gaining more experience with particular skills) and breadth of practice (spreading experience across an array of activities). Better understanding how training for a particular skill set is impacted by periods of focus on a different skill set would allow improved curriculum and assessment design, thereby enhancing the efficiency of training and effectiveness of care. To this end, learning curves were used to compare performance in surgery after prolonged periods of practice to performance after gaps in surgical training. METHOD: Daily operative assessments from the Dalhousie obstetrics and gynecology program were analyzed retrospectively and learning curves were generated. In addition to examining the variability in learning trajectories, the impact of gaps was systematically assessed by comparing resident scores after 2 successive months in which they were not assessed operatively to those collected after 2 successive months in which they were assessed at least once. RESULTS: Four thousand four hundred sixteen scores for 33 residents over a 10-year period were analyzed. Trajectories and peak performances were identified. Residents performed better during their third sequential month of being assessed (mean = 4.40, 95% CI = 4.33-4.46) relative to during months following a period of being away from the operating room for at least 2 months (mean = 4.21, 95% CI = 4.13-4.29; P < .01; d = 0.7). However, maximum performance achieved was more strongly related to the number of times residents experienced a gap in training (r = 0.50) than to the number of times residents experienced 3 consecutive months of training (r = 0.25). CONCLUSIONS: Distinct patterns of development exist for individual residents. Time away from surgical practice and assessment negatively impacted short-term performance, but may improve long-term learning trajectories. This speaks to the value of spaced education and is important for the design of longitudinal skills-based training programs.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".