Going against the grain: An exploration of agency in medical learning
Bibliographic record
Abstract
BACKGROUND: Learner-centred medical education relies on learner agency. While attractive in principle, the actual exercise of agency is a complicated process, potentially constrained by social norms and cultural expectations. In this study, we explored what it means to be an agentic learner in medicine, and how individuals experience and harness agency in their learning. METHODS: Using a constructivist grounded theory approach, we interviewed 19 physicians or physicians-in-training who identified as 'learning mavericks'; this strategy facilitated recruiting participants with a strong sense of themselves as agentic learners. We asked them about atypical learning choices they had made, about support and resistance they encountered and about how they managed to carve a distinct path for themselves. Data collection and analysis were concurrent and iterative, grounded in the constant comparative approach. RESULTS: We identified one overarching concept: agency is work. The work of exercising agency was compounded by a system of professional training that was perceived to promote conformity and to resist individual learner agency. Individuals' capacity to exercise agency appeared to be bolstered by social capital, self-knowledge and mentorship. DISCUSSION AND CONCLUSIONS: Our work extends and elaborates the understanding of learner agency in medicine, highlighting the exercise of agency as a sometimes counter-cultural act that requires learners to resist considerable pressure to conform to social and professional expectations. Agency may come more easily to strong learners who have established their ability to succeed within the system's expectations. Enhancing learner agency thus requires careful attention to learner support. Mentorship that both helps learners to identify appropriate learning paths and shields them from the pull of social expectations may be especially fruitful.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".