From medical history taking to story listening
Bibliographic record
Abstract
The ancient roots for “disease” in English and “病” in Chinese both signify “being unwell” as told from the patient’s story: the English origin of “disease” was “dis-ease”, while “疒” , the Chinese root of “病" , symbolized a sick person resting in bed in a propped up position. Advances in medicine in elucidating the aetiologies of “dis-ease,” often to microscopic and molecular levels, led to the emergence of objective, scientific concept of “disease” from earlier subjective ideas of “dis-ease,” so much so that nowadays more emphasis is being put on structured medical history taking to detect a particular disease pattern rather than listening to the patient’s story of “dis-ease.” Thus, the sick man “disappeared” in the modern society as doctors directed their gaze not on the individual sick person but on the disease of which his or her body was the bearer.” To tune in with the Cadenza symposium theme of “advent of an elder friendly Hong Kong,” adopting a life-story perspective can help in the provision of coordinated services to elders through collaboration and listening to an elder by asking the right question “who is this elder?” (listening to his/her story so as to provide person-centred care), instead of just focusing on “where should he/she be placed?” (a decision that is often resource-driven). In the words of Stephen Watkins, “The purpose of community care is to promote privacy, dignity and independence and provide resources for living. It is a philosophy, not a place.”
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".