P.024 Supporting the transition from trainee to independent neurologist: development of a transition-to-practice clinic for senior neurology residents
Bibliographic record
Abstract
Background: Under Competence by Design (CBD), there are required training experiences (TEs) and entrustable professional activities (EPAs) in the Transition to Practice (TTP) stage. Limited literature exists to support an evidence-based approach to its implementation and evaluation. We created a novel outpatient rotation for PGY5 neurology residents, simulating independent practice and addressing the TTP TEs. Methods: We conducted a needs assessment with informal interviews of senior residents, the program director, and program administrator of our neurology residency program. Guided by Royal College requirements, and available TTP-focused literature, we designed a general neurology clinic run by PGY5 neurology residents. Focuses included increased independence and efficiency, longitudinal follow-up, and applied principles of practice management. Results: Go-live was August 1, 2024. Eight PGY5 residents completed one block, with a second scheduled later in the academic year. Eleven supervisors participated across two sites. Surveys and structured interviews will be used for both groups to evaluate the program, based on the Kirkpatrick Model. Conclusions: Development of a dedicated clinic addressing the TTP TEs in CBD is feasible. Iterative evaluation of the structure, delivery and outcomes of this required TE is critical to ensure that objectives are met and value is added to the residency curriculum.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".