A new tool to assess community-level evidence to inform public health decision making
Bibliographic record
Abstract
Abstract Background As public health responds to evolving challenges around the globe, it is critical to draw on community-level evidence to inform decisions on emerging needs. There are existing tools for assessing the quality of research evidence, but none that explicitly focus on quality assessment of evidence from community sources, including local health status and ever-changing community and political preferences and actions. Methods The National Collaborating Centre for Methods and Tools (NCCMT) in Canada has developed new tools, called Quality Assessment of Community Evidence (QACE), to help public health decision makers assess the quality of community evidence. The QACE tools were drafted through extensive review of existing frameworks, tools and measures for appraising population health and community evidence, and diverse key informants. We identified three consistent themes that became the core dimensions in these tools. By using the QACE tools, practitioners can answer the question: “Is the quality of this evidence about local context, community needs and political preferences good enough to influence decision making?” Results The QACE tools provide probing questions for each of three dimensions: relevant, trustworthy and equity-informed. Supplementary resources help users delve more deeply into different aspects of quality assessment. The QACE tools are intended for public health practitioners who provide and use evidence to support or make decisions about public health practice and policy, including public health practitioners, senior leaders, policy makers and funders. Conclusions The QACE tool is a new addition to the public health toolbox for evidence-informed decision making, providing questions to ask about evidence from community sources. By using the tool as part of a decision-making process, public health practitioners can be assured that their decisions are based on the best-available evidence for their communities. Key messages The new Quality Assessment of Community Evidence (QACE) tools fill the gap in assessing quality of community-level evidence for public health decision-makers. Community evidence, including local health status and needs and community and political preferences and actions, should be assessed for quality in three critical domains.
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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 | 0.340 | 0.668 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.085 | 0.038 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.022 | 0.040 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".