Becoming a clinician: Trainee identity formation within the general practice supervisory relationship
Bibliographic record
Abstract
OBJECTIVES: Becoming a clinician is a trajectory of identity formation in the context of supervised practice. This is a social process where the supervisory relationship is key. Therefore, to know how to support identity formation of clinical trainees, it is necessary to understand how this happens within the supervisory relationship. Our aim was to develop a conceptualisation of trainee identity formation within the general practice supervisory relationship to aid its support. METHODS: We took a critical realist approach using case study design and 'cultural worlds' theory as a conceptual frame. Each case comprised a general practice trainee and supervisor pair. Our data were weekly audiorecordings of interactions between trainee, supervisor and a patient over 12 weeks augmented by post-interaction reflections and sequential interviews. We undertook interpretive analysis using dialogic methods focusing on the doing of language and the cultural discourses expressed. RESULTS: We identified three social discourses centring on: clinical responsibility; ownership of clinical knowledge, and measures of trainee competency. Versions of these discourses defined four trainee-supervisor relational arrangements within which trainee and supervisor assumed reciprocal identities. We labelled these: junior learner and expert clinician; apprentice assistant and master coach, and lead clinician and advisor. We found a trajectory across these identity arrangements. Behind this trajectory was an invitation by the supervisor to the trainee into the social space of clinician and a readiness of the trainee to accept this invitation. Congruence in supervisor and trainee positioning was important. CONCLUSIONS: In the supervisory relationship, trainee and supervisor adopted reciprocal identities. For trainees to progress to identity of 'lead clinician,' supervisors needed to invite their trainee into this space and vacate it themselves. Congruence between supervisor positioning of their trainee and trainee authorship of themselves was important and was aided by explicit dialogue and common purpose. We offer a model and language for trainees, supervisors and departments or schools to facilitate this.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".