Current experience with applying the GRADE approach to public health interventions: an empirical study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach has been adopted by many national and international organisations as a systematic and transparent framework for evidence-based guideline development. With reference to an ongoing debate in the literature and within public health organisations, this study reviews current experience with the GRADE approach in rating the quality of evidence in the field of public health and identifies challenges encountered. METHODS: We conducted semi-structured interviews with individuals/groups that have applied the GRADE approach in the context of systematic reviews or guidelines in the field of public health, as well as with representatives of groups or organisations that actively decided against its use. We initially contacted potential participants by email. Responses were obtained by telephone interview or email, and written interview summaries were validated with participants. We analysed data across individual interviews to distil common themes and challenges. RESULTS: Based on 25 responses, we undertook 18 interviews and obtained 15 in-depth responses relating to specific systematic reviews or guideline projects; a majority of the latter were contributed by groups within the World Health Organization. All respondents that have used the GRADE approach appreciated the systematic and transparent process of assessing the quality of the evidence. However, respondents reported a range of minor and major challenges relating to complexity of public health interventions, choice of outcomes and outcome measures, ability to discriminate between different types of observational studies, use of non-epidemiological evidence, GRADE terminology and the GRADE and guideline development process. Respondents' suggestions to make the approach more applicable to public health interventions included revisiting terminology, offering better guidance on how to apply GRADE to complex interventions and making modifications to the current grading scheme. CONCLUSIONS: Our findings suggest that GRADE principles are applicable to public health and well-received but also highlight common challenges. They provide a starting point for exploring options for improvements and, where applicable, testing these across different types of public health interventions. Several public health organisations are currently testing GRADE, and the GRADE Working Group is eager to engage with these groups to find ways to address concerns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it