Bibliographic record
Abstract
Nous désirons souligner la contribution de sept groupes de boursiers -ainsi que celle des groupes à venir -à l'évolution continue d'une importante expérience de formation.Ces boursiers apportent une vigueur nouvelle au leadership dans le système de prestation de services de santé.Nous tenons également à remercier les enseignants principaux et les enseignants invités qui ont joué un rôle actif et qui ont modelé et peaufiné le programme de formation initial et les méthodes d'enseignement.Les dirigeants et le personnel de la Fondation canadienne de la recherche sur les services de santé ont depuis le début constitué le pivot organisationnel de cet important programme, particulièrement Nina Stipich et Jessie Checkley, qui en ont assumé le leadership, la coordination et la réalisation.Le formidable soutien qu'ont apporté Jennifer Verma, Jasmine Neeson, Kerrie Whitehurst et Beth Everson à la coordination et à la production du présent ouvrage mérite également d'être souligné.Nous désirons également remercier l'équipe de conception et de développement du programme FORCES de 2003 : Steven Lewis,
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 | 0.022 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.228 | 0.106 |
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".