The development and validation of a meta-tool for quality appraisal of public health evidence: Meta Quality Appraisal Tool (MetaQAT)
Bibliographic record
Abstract
OBJECTIVES: Most quality appraisal tools were developed for clinical medicine and tend to be study-specific with a strong emphasis on risk of bias. In order to be more relevant to public health, an appropriate quality appraisal tool needs to be less reliant on the evidence hierarchy and consider practice applicability. Given the broad range of study designs used in public health, the objective of this study was to develop and validate a meta-tool that combines public health-focused principles of appraisal coupled with a set of design-specific companion tools. STUDY DESIGN: Several design methods were used to develop and validate the tool including literature review, synthesis, and validation with a reference standard. METHODS: A search of critical appraisal tools relevant to public health was conducted; core concepts were collated. The resulting framework was piloted during three feedback sessions with public health practitioners. Following subsequent revisions, the final meta-tool, the Meta Quality Appraisal Tool (MetaQAT), was then validated through a content analysis of appraisals conducted by two groups of experienced public health researchers (MetaQAT vs generic appraisal form). RESULTS: The MetaQAT framework consists of four domains: relevancy, reliability, validity, and applicability. In addition, a companion tool was assembled from existing critical appraisal tools to provide study design-specific guidance on validity appraisal. Content analysis showed similar methodological and generalizability concerns were raised by both groups; however, the MetaQAT appraisers commented more extensively on applicability to public health practice. CONCLUSIONS: Critical appraisal tools designed for clinical medicine have limitations for use in the context of public health. The meta-tool structure of the MetaQAT allows for rigorous appraisal, while allowing users to simultaneously appraise the multitude of study designs relevant to public health research and assess non-standard domains, such as applicability.
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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 | 0.545 | 0.757 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.038 |
| Bibliometrics | 0.046 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".