Searching for evidence in public health emergencies: a white paper of best practices
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
Objectives: Information professionals have supported medical providers, administrators and decision-makers, and guideline creators in the COVID-19 response. Searching COVID-19 literature presented new challenges, including the volume and heterogeneity of literature and the proliferation of new information sources, and exposed existing issues in metadata and publishing. An expert panel developed best practices, including recommendations, elaborations, and examples, for searching during public health emergencies. Methods: Project directors and advisors developed core elements from experience and literature. Experts, identified by affiliation with evidence synthesis groups, COVID-19 search experience, and nomination, responded to an online survey to reach consensus on core elements. Expert participants provided written responses to guiding questions. A synthesis of responses provided the foundation for focus group discussions. A writing group then drafted the best practices into a statement. Experts reviewed the statement prior to dissemination. Results: Twelve information professionals contributed to best practice recommendations on six elements: core resources, search strategies, publication types, transparency and reproducibility, collaboration, and conducting research. Underlying principles across recommendations include timeliness, openness, balance, preparedness, and responsiveness. Conclusions: The authors and experts anticipate the recommendations for searching for evidence during public health emergencies will help information specialists, librarians, evidence synthesis groups, researchers, and decision-makers respond to future public health emergencies, including but not limited to disease outbreaks. The recommendations complement existing guidance by addressing concerns specific to emergency response. The statement is intended as a living document. Future revisions should solicit input from a broader community and reflect conclusions of meta-research on COVID-19 and health emergencies.
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.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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