Systematic review methods in environmental health: a critical interpretive synthesis to inform the evolution of systematic review guidance
Bibliographic record
Abstract
Background Systematic reviews are generally regarded as the most reliable and rigorous approach to evidence synthesis. Within the field of environmental health, systematic review methods are used to identify relationships between exposures, exposure mitigation interventions, and health outcomes. However, there are multiple strategies for conducting systematic reviews of exposures. This review aims to provide a comprehensive assessment of current systematic review frameworks, and to characterize similarities and differences between systematic review approaches from across the field of environmental health.Methods We performed an English-language search of MEDLINE via PubMed, EMBASE, and Cochrane databases from January 1, 2013, through March 30, 2023 for systematic review frameworks applied to environmental health research questions. Additionally, we searched 35 organizational websites and references of included studies to identify additional frameworks outside of the peer-reviewed literature. For the critical interpretive synthesis, we purposively sampled and extracted data from frameworks that contributed new information to at least one of the following themes grounded in the PRISMA framework: research question, protocol, search strategy, study selection, data extraction, data synthesis, risk of bias assessment, overall certainty assessment, and reporting findings. Frameworks also addressed disclosure of funding sources and conflict of interest, feasibility considerations, limitations, and directions for future research.Results From 3,417 studies identified through the database search, we included 5 published frameworks. We included another 16 frameworks identified from organizational websites and citation searching; 14 frameworks were included in our purposive sample. Most frameworks (n = 10) originated from North America. Five frameworks addressed all of our predefined themes; all frameworks addressed at least 6 of the 9 themes. Additionally, 9 frameworks described an approach to integrating epidemiologic data with information from animal or in vitro studies. Although we observed variability in whether or how thoroughly frameworks addressed each of the themes, different approaches did not contradict each other within a theme. Rather, frameworks differed in the degree of methodological rigor that was suggested or recommended.Conclusion This systematic review and critical interpretive synthesis provide a comprehensive overview of systematic review approaches in environmental health, proposing necessary domains to guide systematic reviews in environmental health. Operational guidance complements the proposed framework domains. Findings may be useful to researchers who are selecting an approach for their review or developing resources to facilitate the uptake of systematic methods for reviews of environmental exposures.
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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 | 0.748 | 0.857 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.021 | 0.018 |
| Bibliometrics | 0.066 | 0.040 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.014 | 0.025 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".