From intuition to professionalization:a qualitative study about the development of teacher identity in internal medicine senior residents
Bibliographic record
Abstract
Objectives: Professional identity can be defined as a developmental process. Literature suggests that residents aiming to practice in a teaching institution should receive support for the development of teaching competencies as much as they do for medical competencies, but there is limited data on how to recognize teacher identity development in residents. Our study focused on the manifestations of teacher identity development in a group of senior residents in a four-week optional pedagogy rotation. In particular, we were interested in seeing teacher identity development towards the end of the residency program, at a time when clinician identity begins to consolidate.Methods: A qualitative and exploratory approach guided our study design. Participants were internal medicine residents, (from Yr4 to Yr6) at University of Montréal, who intended working in a university setting and were interested in developing a teaching project. Focus groups were held at three separate moments: 1) before rotation, 2) after rotation and, 3) six months after rotation. Direct content analysis was chosen to analyze our findings. Results: We observed the emergence and the evolution of teacher identity and furthermore, we identified six development pathways, which underpin the development of teacher professional identity: 1) From awkward and stereotyped usage to mastery of concepts and teaching techniques, 2) From the reproduction of implicit norms of the clinical setting to the establishment of pedagogical norms 3) From the feeling of powerlessness in teaching to a feeling of mastery and taking initiatives 4)From teaching intuitively to reasoning pedagogically 5) From a teacher based paradigm to the discovery of the learner-centered paradigm and 6) From an emerging identity as a clinician to the simultaneous construction of twin identities:clinician and teacher. Six development indicators providing operational cues to help recognize different facets of teacher identity development were then identified. Conclusion: The identity development pathways allowed us to gain deeper insights about how teacher identity develops in internal medicine residents toward the end of a pedagogical rotation. It is our hope that these findings will help educators recognize and support the development of teacher identity in their residents.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".