Commentary Mindful medical practice: just another fad?
Bibliographic record
Abstract
A thoughtful colleague pointed out that over the past 50 years we have experimented with many different ways to humanize medicine. Recently, a few of our attempts have included explicitly promot-ing empathy,1 inculcating cultural competence,2 and offering courses on narrative medicine.3 This is not to say that any of these approaches have failed, but we are still searching—and recent evidence suggests that medicine retains powerful dehumanizing characteris-tics.4 The continuation of the problem might reflect the challenge that ongoing expansion of medical capabili-ties5 and demand poses to humane care, but it might also reflect an important omission in the humaniz-ing initiatives—an explicit focus on self-care of prac-titioners ’ own humanity. The increasing awareness of burnout and stress among physicians6 and how physi-cian well-being affects patient care7 might explain the developing interest in mindfulness, one of the few self-care practices for which there is empirical evidence of benefit.8 A PubMed search of the terms mindfulness and mindful revealed the following trend: 10 articles pub-lished between 1969 and 1978; 22 articles published between 1979 and 1988; 93 articles published between 1989 and 1998; and 300 articles published between 1999 and 2008 (including 80 in 2008). In addition, we found that 16 medical schools in North America, including Harvard, Duke, and McGill, offer courses on mindfulness to medical students and health care prac-titioners. Our purpose here is to point out some fea-tures of mindfulness that could threaten its long-term viability in medicine, while clarifying its potential role in improving medical practice.
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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 | 0.015 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.035 | 0.038 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".