Methods to support evidence-informed decision-making in the midst of COVID-19: creation and evolution of a rapid review service from the National Collaborating Centre for Methods and Tools
Bibliographic record
Abstract
BACKGROUND: The COVID-19 public health crisis has produced an immense and quickly evolving body of evidence. This research speed and volume, along with variability in quality, could overwhelm public health decision-makers striving to make timely decisions based on the best available evidence. In response to this challenge, the National Collaborating Centre for Methods and Tools developed a Rapid Evidence Service, building on internationally accepted rapid review methodologies, to address priority COVID-19 public health questions. RESULTS: Each week, the Rapid Evidence Service team receives requests from public health decision-makers, prioritizes questions received, and frames the prioritized topics into searchable questions. We develop and conduct a comprehensive search strategy and critically appraise all relevant evidence using validated tools. We synthesize the findings into a final report that includes key messages, with a rating of the certainty of the evidence using GRADE, as well as an overview of evidence and remaining knowledge gaps. Rapid reviews are typically completed and disseminated within two weeks. From May 2020 to July 21, 2021, we have answered more than 31 distinct questions and completed 32 updates as new evidence emerged. Reviews receive an average of 213 downloads per week, with some reaching over 7700. To date reviews have been accessed and cited around the world, and a more fulsome evaluation of impact on decision-making is planned. CONCLUSIONS: The development, evolution, and lessons learned from our process, presented here, provides a real-world example of how review-level evidence can be made available - rapidly and rigorously, and in response to decision-makers' needs - during an unprecedented public health crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.689 | 0.845 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.058 | 0.053 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.040 | 0.027 |
| Open science | 0.016 | 0.026 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.025 | 0.021 |
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".