Exploring Residentsʼ Perceptions of Expertise and Expert Development
Bibliographic record
Abstract
BACKGROUND: Given the expectation that trainees develop into adaptive experts, able to effectively solve both routine and nonroutine problems of practice, and that they do so by actively guiding and shaping their own learning, the purpose of this study was to explore how residents at the postgraduate level of training conceptualize expertise, expert development, and their own learning in the developing expert trajectory. METHOD: This research was a grounded theory study conducted during an 11-month period at a large, urban, Canadian university. RESULTS: Three major themes were identified from the data analysis: (1) the dominance of routinization as the pathway to expert practice, (2) a sophisticated conceptualization of the role and complexity of routine practice, and (3) a recognition that nonroutine problems are an important part of physician practice. CONCLUSIONS: The results highlight our participants' emerging understanding of the complementary nature of routine and nonroutine problem solving by demonstrating their engagement in a process of progressive problem solving, as well as their inclusion of nonroutine problem solving as a crucial part of expert practice.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".