Public health priority setting on a national scale: The Scottish experience
Bibliographic record
Abstract
Objectives: Scotland has the lowest life expectancy in Western Europe and significant health inequalities. A national review of public health in 2015 found that there was a lack of coherent action across organisational boundaries, inhibiting progress. This paper describes a rapid (four-month) systematic approach to prioritisation of Scotland's public health challenges, which was evidence-based, transparent and made use of significant stakeholder engagement. Study design: Cross-sectional survey of stakeholders in deliberative meetings. Methods: An independent Expert Advisory Group (EAG) was formed to develop a typology of public health priorities, a long-list of potential priorities and ranking criteria. Deliberative stakeholder events were held at which the criteria were refined and priorities scored by participants from a wide range of stakeholder organisations. Results: The proposed typology identified three types of public health priorities: risk factors, social factors and system factors; medically defined disease entities were not used deliberately, to facilitate broad stakeholder participation. Fifteen criteria were identified to help identify priority issues, based on the scope of their burden, amenability to change, and multi-stakeholder preferences. Six public health priorities were selected by the EAG based on stakeholder scoring of a long-list against these criteria. Conclusion: Prioritisation is important in modern public health but it is challenging due to limited data availability, lack of agreed evidence on effectiveness and efficiency of interventions, and divergent stakeholder views. The Scottish experience nevertheless shows that useful public health priorities can be agreed upon by a wide range of stakeholders through a transparent, participatory and logical process.
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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 | 0.038 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".