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 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".