Bibliographic record
Abstract
Au moment d’accepter le prix de conférence Schering, nous avons examiné le thème de notre conférence annuelle en réfléchissant sur ce qu’avaient été nos expériences durant l’élaboration, la mise en place et la diffusion des ordonnances collectives relatives aux patients en soins palliatifs de toute notre région. Certes, nous avons eu recours à nos coeurs, à nos esprits et à nos voix, mais en plus de tout cela, à une certaine dose de courage pour effectuer un changement de pratique piloté par des infirmières. Cette conférence intégrera les éléments fondamentaux que sont le courage, le coeur et l’intelligence, les reliant au cadre théorique conçu par Alison Kitson et ses collègues (Kitson, McCormack et Harvey, 1998) et qui vise à favoriser une pratique fondée sur les données probantes. Alors que nous nous transportons « quelque part audelà de l’arc-en-ciel », autrement dit à North Simcoe Muskoka, et que nous décrivons notre périple, suivez-nous à la recherche du merveilleux magicien d’Oz (Baum, 1939).
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".