9.K. Workshop: Public health monitoring and reporting – Examples of how to fill the gaps of health inequalities
Notice bibliographique
Résumé
Abstract There is a need for cross national border exchange of experience by sharing best practices for monitoring and reporting on public health with a sustained driving force, to ensure that evidence-based approaches are continuously improving and informing best practices for reducing inequality and inequity gaps. By doing this, the emerging field of evidence based public health programming, covering different aspects of inequalities and unequal distribution of determinants of health, is improved. The workshop intends to introduce a global and intercontinental collaborative approach to jointly identify necessary tools and understand the mechanisms of monitoring and reporting on public health, to combat health inequalities. The workshop will encourage the building of practical culture and community of public health professionals to share lessons, evidence and best practices. It will also enable the support of ongoing assessment, communication of gaps in health that are emerging and caused by barriers at different levels of societies. There is need for an increased understanding of the emerging public health threats in contexts, such as increasing inequalities in health and social determinants of health, climate change disasters, disease outbreaks, influx of migration and political popularism threatening evidence informed decision making and policies. Despite being high-income countries with universal health coverage Australia, Canada and Sweden share similar public health challenges. The interactive workshop intends to contribute to an exchange of experiences from countries that are geographically located far from each other with differently organized health systems but united with a common agenda to act on health inequalities. The exchange of shared knowledge and experiences between the participating countries will shed light and focus on functionality of public health monitoring and reporting mechanisms and tools used in the above-mentioned countries. This will be a way of identifying areas of improvement in addressing inequality gaps. Evidence based interventions in public health depend on solid monitoring, analysis and reporting frameworks. With continuous changes in the public health environment, improvements on what and how public health is analysed is needed to identify existing gaps. To further address equity, with a focus on vulnerable groups for improved public health, solid public health monitoring and reporting mechanisms are vital to supporting credible advocacy and policy actions. Key messages Monitoring and reporting health and social determinants of health are imperative ingredients of decision-making. A joint approach to use monitoring tools to improve global public health is needed. Countries geographically located far from each other, with differently organized health systems but similar public health challenges are united with a common agenda to act on health inequalities.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,034 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,005 | 0,011 |
| Intégrité de la recherche | 0,008 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,013 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».