“You’re a trainee telling your consultant to hold their question until later“: Using a resident-led faculty development workshop to explore trainee-consultant expertise role-reversal
Bibliographic record
Abstract
Background: Medical education traditionally involves directional flow of knowledge/skills/attitudes from a senior to junior individual. However, medical training also provides opportunities for expertise role-reversal, where the direction of flow is reversed. Unlike fields such as aviation, medicine has not yet begun to fully realise the educational potential of this approach. Objective: To better understand how role-reversal is viewed by medical education participants, necessary for its use as a tool to advance both education and patient care. Methods: A senior resident designed and led a feedback-writing workshop for her own consultants (conducted 2022). After the session, eight consultants were interviewed in a semi-structured format. Analysis was conducted using the Stenfors-Hayes phenomenographical approach. Results: A multiplicity of experiential perspectives was identified by both consultants (teacher/participant/supporter/hierarchy member/colleague/holder of multiple perspectives) and trainee (presenter/subordinate/learner/researcher). The exercise increased appreciation and awareness of the complexity of the trainee-consultant educational-power relationship, though both parties maintained traditional hierarchy despite altered informational flow. Participants often held multiple articulated experiential perspectives simultaneously. Conclusions: Consultants were able to assume a learning mindset while simultaneously maintaining awareness of their existing hierarchical relationship to the trainee-presenter; the trainee, conversely, struggled to adopt the teacher mindset. Deliberately viewing moments where trainees present new information to consultants as expertise role-reversal may provide a starting point for more equitable knowledge exchange between both parties in the clinical routine, and a foil for epistemic injustice. Increasing recognition and use of expertise role reversal can play a critical role in improving educational culture.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".