9.K. Workshop: Public health monitoring and reporting – Examples of how to fill the gaps of health inequalities
Bibliographic record
Abstract
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.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".