Intervention des pharmaciens en cas de catastrophe naturelle : Vue intérieure du travail des pharmaciens et de leur rôle dans la société
Bibliographic record
Abstract
Dans le present numero, nous publions un article de Mme Denise A. Epp, une Canadienne qui vit au Japon et qui y etudie en vue d’obtenir son doctorat en sciences pharmaceutiques. Elle nous raconte comment les pharmaciens au Japon et au Canada sont intervenus dans de grandes catastrophes. A la suite de la catastrophe nucleaire resultant du tsunami cause par le seisme de Tohoku, M. Yoshirou Tanno, en depit de ses epreuves personnelles, a immediatement participe a la mise sur pied du centre d’evacuation d’urgence a l’ecole locale, a coordonne les interventions des pharmaciens benevoles qui aidaient les gens a obtenir leurs medicaments et a aide a etablir une pharmacie temporaire. Dans son article, Mme Epp nous raconte egalement l’histoire d’Anita et Bob Brown, d’Okotoks en Alberta, qui ont fourni des services aux patients de High River, une ville voisine durement touchee par les inondations de juin 2013. Mme Brown a ete l’une des premieres pharmaciennes d’Alberta a recevoir son autorisation de prescrire des medicaments et le centre de sante local, depasse par la demande, a commence a lui envoyer des patients. Les Brown ont courageusement mobilise leurs employes pour prodiguer des soins aux nombreux patients deplaces qui avaient besoin de leur aide. Lisez cet article reconfortant et inspirant.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".