Developing Confidence in Uncertainty: Conflicting Roles of Trainees as They Become Educators in Veterinary and Human Medicine
Bibliographic record
Abstract
The important role of medical trainees (interns and residents) as teachers is increasingly recognized in veterinary and human medicine, but often is not supported through adult learning programs or other preparatory training methods. To develop appropriate teaching programs focused on effective clinical teaching, more understanding is needed about the support required for the trainee's teaching role. Following discussion among faculty members from education and veterinary and pediatric medicine, an experienced external observer and expert in higher education observed 28 incoming and outgoing veterinary and pediatric trainees in multiple clinical teaching settings over 10 weeks. Using an interpretative approach to analyze the data, we identified five dynamics that could serve as the foundation for a new program to support clinical teaching: (1) Novice-Expert, recognizing transitions between roles; (2) Collaboration-Individuality, recognizing the power of peer learning; (3) Confidence-Uncertainty, regarding the confidence to act; (4) Role-Interdisciplinarity, recognizing the ability to maintain a discrete role and yet synthesize knowledge and cope with complexity; and (5) Socialization-Identity, taking on different selves. Trainees in veterinary and human medicine appeared to have similar needs for support in teaching and would benefit from a variety of strategies: faculty should provide written guidelines and practical teaching tips; set clear expectations; establish sustained support strategies, including contact with an impartial educator; identify physical spaces in which to discuss teaching; provide continuous feedback; and facilitate peer observation across medical and veterinary clinical environments.
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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".