AfterWards: A Narrative Medicine Program at Johns Hopkins Medicine and in China
Bibliographic record
Abstract
Narrative medicine is a multidisciplinary field of inquiry and practice based on the premise that medical care takes place in the context of stories. Research on narrative medicine training suggests that it conveys benefits such as improved communication skills and personal and professional growth to physicians, medical students, and other health care providers. Narrative medicine can promote empathy and trust between patients and physicians and foster self-care. In 2014, the author and a colleague started an ongoing inter-disciplinary narrative medicine program in the Children’s Center of the Johns Hopkins Hospital called AfterWards. The program, which meets monthly, is open to all on a volunteer basis. Through literature, art, and writing, AfterWards nurtures empathy, encourages reflective practice, and builds community among a diverse group of health care providers. Through a series of lectures and workshops at Johns Hopkins Medicine, Peking Union Medical College, and Fudan Hospital in Shanghai, the author has introduced AfterWards to Chinese medical educators and clinicians. Working with Dr. Marta Hanson, she created an AfterWards Facilitator’s Guide for the use of Chinese practitioners. A recent White Paper on Chinese health care indicates that an infusion of humanities-based education, of which narrative medicine forms a part, can help rebuild patient-physician trust. Recently, there has been an increase in interest in narrative medicine in the United States and China. However, more research is needed to demonstrate the impact of programs like AfterWards. Challenges to the implementation of narrative medicine programs remain, most significantly in terms of expertise, resources, and time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".