Bibliographic record
Abstract
Les patients reconnaissants offrent souvent des cadeaux a leur medecin de famille, un geste en apparence simple qui peut soulever des questions complexes dans la relation therapeutique. Il n’y a pas de regles definitives concernant l’acceptation de cadeaux de la part de patients et les opinions a ce sujet sont divergentes. Certains croient que les medecins ne devraient jamais accepter de cadeaux parce que ce don pourrait influencer les standards de soins ou affaiblir la relation de fiduciaire. D’autres sont d’avis que l’acceptation de cadeaux dans certaines circonstances permet aux patients d’exprimer leur gratitude et renforce les liens medecin-patient. Il vaut la peine d’examiner les arguments de part et d’autre sur le plan de l’ethique et de prendre en consideration la facon dont la decision d’accepter ou non des cadeaux pourrait etre influencee par des facteurs comme la nature et la duree de la relation medecin-patient, le cout, le genre de cadeau et le moment de l’offrir, de meme que la motivation apparente du geste.
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 | 0.020 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 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".