Dialogues on the Threshold: Dialogical Learning for Humanism and Justice
Bibliographic record
Abstract
Given the constant pressures of overflowing clinics, hospital wards, and emergency departments; shortened duty hours; and increased accreditation requirements, overburdened clinician teachers ask, "How does one teach for humanism and justice?" How does one step away-even momentarily-and focus teaching on the individual in front of us, the person who requires our attention and care? This approach must not only involve content (the patient's perspective of illness, social context, and life story) but also must be tightly linked with the ways in which these lessons in living are learned and taught. In this article, the authors propose recognition and use of a style of communication that is already implicitly present in clinical conversations and that is uniquely capable of stimulating reflection on the human dimensions of medicine: that of dialogue.Dialogue involves committing one's whole self to communicative exchange and emphasizes interpersonal relationships and trust. Its result is often not a specific answer; rather, it is enhanced understanding through the generation of new questions and possibilities and action in implementing solutions. It requires a reorientation of the teacher-learner relationship from top-down to one of open exchange and shared authority and responsibility. In the context of professional identity development, these conversations become dialogues on the threshold of transformative change. Through an exploration of dialogical teaching, the authors envision clinical education as constantly stepping in and out of goal-oriented discussions and reflective dialogues, all with the overall goal to educate physicians who practice with excellence, compassion, and justice.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.050 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".