Assessing supervisor versus trainee viewpoints of entrustment through cognitive and affective lenses: an artificial intelligence investigation of bias in feedback
Open accessAbstract
The entrustment framework redirects assessment from considering only trainees’ competence to decision-making about their readiness to perform clinical tasks independently. Since trainees and supervisors both contribute to entrustment decisions, we examined the cognitive and affective factors that underly their negotiation of trust, and whether trainee demographic characteristics may bias them. Using a document analysis approach, we adapted large language models (LLMs) to examine feedback dialogs (N = 24,187, each with an associated entrustment rating) between medical student trainees and their clinical supervisors. We compared how trainees and supervisors differentially documented feedback dialogs about similar tasks by identifying qualitative themes and quantitatively assessing their correlation with entrustment ratings. Supervisors’ themes predominantly reflected skills related to patient presentations, while trainees’ themes were broader—including clinical performance and personal qualities. To examine affect, we trained an LLM to measure feedback sentiment. On average, trainees used more negative language (5.3% lower probability of positive sentiment, p < 0.05) compared to supervisors, while documenting higher entrustment ratings (+ 0.08 on a 1–4 scale, p < 0.05). We also found biases tied to demographic characteristics: trainees’ documentation reflected more positive sentiment in the case of male trainees (+ 1.3%, p < 0.05) and of trainees underrepresented in medicine (UIM) (+ 1.3%, p < 0.05). Entrustment ratings did not appear to reflect these biases, neither when documented by trainee nor supervisor. As such, bias appeared to influence the emotive language trainees used to document entrustment more than the degree of entrustment they experienced. Mitigating these biases is nonetheless important because they may affect trainees’ assimilation into their roles and formation of trusting relationships.
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In a study analyzing feedback exchanges between students and clinical advisors using LLMs to assess sentiment and investigate potential biases, students were found to use more negative language than their advisors did.
“Another study analyzed feedback exchanges between students and clinical advisors, utilizing LLMs to assess sentiment and investigate potential biases in feedback [148]; as a result, students were found to use more negative language than their advisors did.”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [148]
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- Authors
- Gin, B. C., ten Cate, O., O’Sullivan, P. S., & Boscardin, C.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Gin, Brian C.
- Venue
- Advances in Health Sciences Education
- Published
- 2024-02-23
- Volume
- 29
- Issue
- 5
- Pages
- 1571-1592
- ISSN
- 1382-4996 (ISSN-L)
- 1573-1677
- PMC
- PMC11549112
- OpenAlex
- W4392773752
- Cited by
- 14
- References
- 73
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 3
- FWCI
- 5.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Advances in Health Sciences Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Social Sciences › Gender Studies › Diversity and Career in Medicine
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- entrustment, clinical supervision, medical trainees, supervisor feedback, large language models, gender bias, underrepresented in medicine, emotional factors, cognitive factors, clinical competence assessment, medical education
- MeSH terms
- Formative Feedback, Artificial Intelligence, Clinical Competence, Cognition, Feedback, Female, Humans, Male, Students, Medical, Bias, Trust
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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