Struggling productively with professionalism: Swinging the pendulum between behaviors and identity
Bibliographic record
Abstract
Professionalism has long been a contentious and evolving focus in medical education, with a long history of educators grappling with definitions, concepts and frameworks. Drawing on over two decades of scholarship and personal reflection, this article traces the trajectory of professionalism assessment in medicine, using the concept of "productive struggle" as a guiding frame. Initially championing behavioural frameworks as more objective and assessable than character-based definitions, researchers came to recognize the complexity and contextual nature of professional conduct. Both learners and faculty struggle to define and assess which behaviours are most professional, and behaviours alone may be insufficient signals of professionalism without insight into underlying rationales and the environments in which actions occur. As identity formation gained traction-emphasizing internalization of professional values-the pendulum swung away from behaviours. While conceptually appealing, this shift brought its own tensions, including learner resistance, concerns about surveillance and performance of professionalism, and conflicts between personal and professional identities. The author highlights growing discomfort among both learners and supervisors, especially when professionalism is used punitively or when wellness and discomfort are falsely positioned as mutually exclusive. Ultimately, the author promotes a "both-and" approach that integrates behaviours and identity, while acknowledging contextual influences and allowing space for growth. This will hopefully lead to the pendulum swinging in shorter arcs, towards a middle ground. In the meantime, we are faced with eager learners who want to develop as professionals while maintaining their own health and intersecting identities, and faculty who respect this yet are finding it increasingly challenging to promote productive struggle.
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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".