Understanding the Influence of the Junior Attending Role on Transition to Practice: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The Junior Attending (JA) role is an educational model, commonly implemented in the final years of training, wherein a very senior resident assumes the responsibilities of an attending physician under supervision. However, there is heterogeneity in the model's structure, and data are lacking on how it facilitates transition to independent practice. OBJECTIVE: The authors sought to determine the value of the JA role and factors that enabled a successful experience. METHODS: The authors performed a collective case study informed by a constructivist grounded theory analytical approach. Twenty semi-structured interviews from 2017 to 2020 were conducted across 2 cases: (1) Most Responsible Physician JA role (general internal medicine), and (2) Consultant JA role (infectious diseases and rheumatology). Participants included recent graduates who experienced the JA role, supervising attendings, and resident and faculty physicians who had not experienced or supervised the role. RESULTS: Experiencing the JA role builds resident confidence and may support the transition to independent practice, mainly in non-medical expert domains, as well as comfort in dealing with clinical uncertainty. The relationship between the supervising attending and the JA is an essential success factor, with more productive experiences reported when there is an establishment of clear goals and role definition that preserves the autonomy of the JA and legitimizes the JA's status as a team leader. CONCLUSIONS: The JA model offers promise in supporting the transition to independent practice when key success factors are present.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".