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Record W7131847195 · doi:10.1093/acamed/wvaf067

Deciphering signals: exploring how preceptor behaviors shape learner mindset

2025· article· en· W7131847195 on OpenAlexaff
Robin Mackin, Chris Watling

Bibliographic record

VenueAcademic Medicine · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsRoyal College of Physicians and Surgeons of CanadaWestern University
Fundersnot available
KeywordsMindsetPerceptionDimension (graph theory)PreceptorProcess (computing)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.315
GPT teacher head0.489
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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