Bibliographic record
Abstract
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 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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".