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Record W4210457975 · doi:10.1097/acm.0000000000002846

A Matter of Trust

2019· article· en· W4210457975 on OpenAlexaff
Elizabeth Hendren, Arno K. Kumagai

Bibliographic record

VenueAcademic Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsThe Wilson CentreWomen's College Hospital
Fundersnot available
KeywordsPreceptorCompetence (human resources)PsychologyDutyPerceptionUnconscious mindAgency (philosophy)Social psychologyMedical educationMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Trust is a fundamental tenet of the patient-physician relationship and is central to providing person-centered care. Because trust is profoundly relational and social, building trust requires navigation around issues of power, perceptions of competence, and the pervasive influence of unconscious bias-processes that are inherently complex and challenging for learners, even under the best of circumstances. The authors examine several of these challenges related to building trust in the patient-physician relationship. They also explore trust in the student-teacher relationship. In an era of competency-based medical education, a learner has the additional duty to be perceived as "entrustable" to 2 parties: the patient and the preceptor. Dialogue, a relational form of communication, can provide a framework for the development of trust. By engaging people as individuals in understanding each other's perspectives, values, and goals, dialogue ultimately strengthens the patient-physician relationship. In promoting a sense of agency in the learner, dialogue also strengthens the student-teacher relationship by fostering trust in oneself through development of a voice of one's own.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.018
GPT teacher head0.353
Teacher spread0.335 · 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; both teacher heads agree on what is shown here.

Study designObservational
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".

Quick stats

Citations32
Published2019
Admission routes1
Has abstractyes

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