Bibliographic record
Abstract
Back to table of contents Previous article Next article International NewsFull AccessFrench Psychiatrist Promotes Fatty Acids For Depression CareJoan Arehart-TreichelJoan Arehart-TreichelPublished Online:19 Mar 2004https://doi.org/10.1176/pn.39.6.0020aSome scientific evidence suggests that omega-three fatty acids can counter depression and perhaps bipolar disorder (Psychiatric News, August 3, 2001; January 16). But in France, interest in the omega-three fatty acids is extending far beyond the scientific lab.French psychiatrist David Servan-Schreiber, M.D., has written a best-selling book called Guérir—le stress, l'anxiété, et la dépression sans médicaments ni psychanalyse ( To Heal—Stress, Anxiety, and Depression Without Medication and Without Psychoanalysis).In this book, he touts the omega-three fatty acids as a natural treatment for depression. So far, the book has sold some 380,000 copies and has prompted a number of French people to buy omega-three fatty acids, according to a February 18 French television report.Servan-Schreiber is quoted as saying on a University of Laval, Quebec, Web site: "Antidepressants are a great discovery, and they are very useful in certain cases. My book is against nothing. It is for research into efficacious natural methods and for their integration into the practice of medicine." ▪ ISSUES New Archived
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.446 | 0.243 |
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".