Benefits and challenges of communicating long-term data in public health, SSPH+ Workshop - Final Report
Bibliographic record
Abstract
Over the past 150 years, there have been major improvements in the health and wealth of populations across the world, supported partly by progress in public health. In the context of recent crises (COVID-19 pandemic, climate change, …) and infodemic, pessimistic narratives about the population health status are spreading, overshadowing these achievements and weakening trust in scientific and evidence-based institutions. We gathered population health scientists, policymakers and communication experts to demonstrate the benefits of using and communicating long-term data in population health surveillance, and to discuss the challenges arising from such data. The benefits of communicating long-term public health data include illustrating sustained progress across populations andtime, revealing trends, identifying emerging problems, uncovering disparities and guiding resources. Participants of the workshop discussed many challenges encountered when communicating long-term data in public health: the uncertainty and missing values inherent to these data, the dynamic nature of such data, the current era of infodemic and the need to tailor messages for different audiences with different levels of expertise. To tackle these challenges, we discussed the importance of transparency, of building and maintaining trust in scientific and evidence-based institutions, and of communicating adequately by tailoring messages to the target audience, providing context, and delivering clear key messages.
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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 | 0.040 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".