Prendre le virage des partenariats
Bibliographic record
Abstract
Deux projets démontrent que la mise en œuvre de données colligées sur le terrain peut contribuer à régler des problèmes dans le milieu de la santé pour favoriser de meilleurs résultats et de plus grandes efficiences. Dans le premier exemple, une vaste coalition de partenaires publics et privés de l'Alberta recourt aux techniques de mesures améliorées et à la méthodologie du Triple objectif pour améliorer les résultats cliniques de populations de cas complexes et lourds du quartier Eastwood d'Edmonton. On espère que les conclusions novatrices qui en sont tirées seront adaptées à d'autres régions de la province. Dans le deuxième exemple, la Childhood Obesity Foundation s'associe à Merck au Canada et à Ayogo (une société de thérapies numériques située à Vancouver) et utilise le concept novateur de la « ludification » pour mobiliser les jeunes de plus en plus sédentaires du Canada et modifier leurs comportements.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".