Deciphering signals: exploring how preceptor behaviors shape learner mindset
Bibliographic record
Abstract
PURPOSE: Medical education has embraced Dweck's theory of a growth mindset because it reflects a commitment to developmental progression. The benefits of a growth mindset can be difficult to realize within medicine's professional culture, which may constrain its adoption and expression. To date, strategies to nurture a growth mindset have been directed toward changing the behavior of individual learners, which is insufficient. Preceptor behaviors shape the learning culture, but their influence on learner attitudes toward the learning process is unexplored. METHOD: The authors conducted a qualitative study using constructivist grounded theory methodology. Seventeen learners from Western University were interviewed in 2023. An iterative process was employed whereby data collection and analysis took place concurrently. Dweck's theory of mindset was used as a sensitizing concept. Open coding was followed by more focused coding, and ideas both within and across categories were compared to inform generation of theory. A reflexivity lens was applied throughout. RESULTS: Learners are constantly interpreting signals and using them to form impressions about their preceptors' value systems. These signals are conveyed in a preceptor's behavior, and learners often adapt their learning behaviors accordingly. When a preceptor is perceived primarily to value learner growth, learners will adopt behaviors in line with a growth mindset. When a preceptor is perceived primarily to value displays of competence over growth, learners may adopt behaviors in line with a fixed mindset. Furthermore, in the absence of growth-valuing signals, learners tend to default to impression management and may exhibit behaviors in keeping with a fixed mindset. CONCLUSIONS: This study offers an important new dimension to our understanding of the dynamic nature of mindsets: that learner mindsets may be preceptor-responsive, shifting in response to perceptions about preceptors' values. These new insights can inform future efforts to foster a growth mindset within medicine's professional culture.Teaser text: This study explores how learners' mindsets are influenced by preceptor attitudes and behaviors relevant to the learning process and offers an important new dimension to our understanding of the dynamic nature of mindsets: that learner mindsets may be preceptor-responsive, shifting in response to perceptions about preceptors' values.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".