Upstream Medicine : Doctors for a Healthier Society : [book supplements]
Bibliographic record
Abstract
Original French-language contributions, by Claudel Pétrin-Desrosiers, “Chapter 10: Interview with Simon-Pierre Landry” and “Chapter 12: Interview with Joanne Liu,” and by Nina Nguyen, “Chapter 21: Interview with Alain Vadeboncoeur.” Versions françaises des chapitres par Claudel l Pétrin-Desrosiers, “Chapitre 10 : Entretien avec Simon-Pierre Landry” et “Chapitre 10 : Entretien avec Joanne Liu” et par Nina Nguyen, "Chapitre 21 : Entretien avec Alain Vadeboncoeur.” Book description: Alleviating suffering and treating patients, whether in a physician’s office or an emergency room, can be a matter of prescribing medication or staunching a bleeding wound. But every clinical story has a social story, and patients who present with acute medical problems or a chronic disease often describe the everyday life conditions that made them sick in the first place. These stories are often about where they work, live, and play, and about their income, food security, and housing. Doctors are now listening. More than this, they are working with their patients and communities to address the root causes of illness at their sources. Upstream Medicine features interviews by medical students and residents with leading Canadian physicians whose practices bring evidence-based, upstream ideas to life. Their personal stories and patient encounters illuminate the social determinants of health – the conditions that lead to good health and long lives or to more complex, painful, and expensive downstream medical problems later on. By transforming how we imagine the practice of medicine, this book will help us build a healthier society. Original French-language chapters in the book.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.056 |
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".