Books: <i>Family Medicine. The Medical Life History of Families</i>
Bibliographic record
Abstract
WEAVING THROUGH GENERATIONSMy parents' surgery was in an extension of our home.As children, we answered the phone, gave out prescriptions at the front door when the surgery was closed, and often helped with filing letters or doing other paperwork during school holidays.Growing up, we came to know the patients of the practice and saw how health and sickness weaved through generations.So, when I first read Family Medicine: The Medical Life History of Families by Frans Huygen, I almost recognised these patients, though from a different country and a very different culture.Describing his patients in a way that we probably could not do now, he shared their personal lives, the family dynamics, how illness repeated in mothers and daughters, the impact of caring for patients at home, and the relationships that are so much a part of family medicine.However, it was his charts and diagrams recording sickness through families that were groundbreaking in a time long before electronic records; his deep understanding of psychological pathology predated our insights into depression; and his drawings illustrating the book, and a further sketchbook Herinneringen aan Lent, capture the burden of illness more acutely than any textbook.Frans was the grandfather of scholarly general practice in the Netherlands who
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.065 |
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".