Evidence-informed decision making in public health in action
Bibliographic record
Abstract
You have been asked by your local health department to join a team looking at ways to increase physical activity in adults and children.A recent report suggests that there is an increase in overweight and obesity in the local population.Your health department includes a largely urban, socioeconomically and ethnically diverse population.As an Environmental Health Specialist, you want to see if the built environment or urban planning interventions could have an effect on rates of physical activity and, ultimately, improve the physical health of your population.Where do you begin?What is evidence-informed public health?Evidence-Informed Public Health (EIPH) is the process of distilling and disseminating the best available evidence from research, context and experience, and using that evidence to inform and improve public health practice and policy.Put simply, it means finding, using, and sharing what works in public health.The National Collaborating Centre for Methods and Tools (NCCMT) recommends a seven-step process of EIPH (NCCMT 2012).This paper will explain the steps in the process and recommend tools to help at each step.
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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 | 0.281 | 0.412 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.024 | 0.025 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".