Contrer la réticence face à la vaccination dans les programmes de vaccination, les cliniques et les cabinets
Bibliographic record
Abstract
Le présent point de pratique contient des conseils fondés sur des données probantes à l’intention des programmes de vaccination provinciaux et territoriaux, des cliniques et des cabinets afin de contrer la réticence face à la vaccination et d’améliorer les taux de vaccination. Les étapes à privilégier s’établissent comme suit : 1) définir les sous-groupes sous-vaccinés (ce qui exige la tenue de registres) et les interventions diagnostiques et ciblées; 2) enseigner les pratiques exemplaires à tous les dispensateurs de soins qui participent à la vaccination; 3) faire appel à des stratégies fondées sur des données probantes pour accroître les taux de vaccination, y compris les rappels, un emplacement des cliniques et des heures d’ouverture pratiques et des communications adaptées; 4) informer les enfants, les adolescents et les adultes de l’importance de la vaccination pour la santé et 5) travailler en collaboration avec les régions sociosanitaires provinciales et territoriales et avec le gouvernement fédéral, les organismes non gouvernementaux, les leaders communautaires et les services de santé.
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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 | 0.108 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".