Chinese Sources for AfterWards: From Premodern Poetry, Paintings, and Medical Texts to Modern Novels, Film, and Documentaries
Bibliographic record
Abstract
This paper focuses on Chinese sources suggested for a narrative medicine (NM) program, called AfterWards. Dr Lauren Small established AfterWards in 2014 and has been coordinating it since out of the Pediatrics Department at Johns Hopkins Medicine. In early 2019, she started giving a series of lectures and workshops about AfterWards to Chinese medical educators and clinicians in Beijing and Shanghai. She created an AfterWards Facilitator’s Guide based on Western-language sources for workshop participants. She also started to organize with Jiang Yuhong (Peking Union Medical College) a workshop for Chinese colleagues to be held at Johns Hopkins Medicine in October 2019. They invited the author to participate. The idea was hatched then to develop Chinese source materials following the AfterWards structure for an updated Facilitator’s Guide that Dr Small had initially written. A typical one-hour AfterWards session consists of a specific five-part structure: a literary text or artwork, an associated theme, discussion topics, a writing exercise, and shared reflection. While the content of the program always changes from session to session, the basic structure remains the same. This paper summarizes the types of Chinese sources and their related narrative-medicine themes that were originally selected for inclusion in the updated AfterWards Facilitator’s Guide intended for Chinese colleagues. These sources about coping with sick family members, aging, and illness ranged from the textual (classical Chinese poems on aging and diagnostic forms for training students) and visual (premodern Chinese paintings and murals of medical encounters) to the fictive (novels) and performative (contemporary Asian-American film in English and Chinese-language film and documentaries).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".