Institutional and behaviour-change interventions to support COVID-19 public health measures: a review by the Lancet Commission Task Force on public health measures to suppress the pandemic
Bibliographic record
Abstract
The Lancet COVID-19 Commission Task Force for Public Health Measures to Suppress the Pandemic was launched to identify critical points for consideration by governments on public health interventions to control coronavirus disease 2019 (COVID-19). Drawing on our review of published studies of data analytics and modelling, evidence synthesis and contextualisation, and behavioural science evidence and theory on public health interventions from a range of sources, we outline evidence for a range of institutional measures and behaviour-change measures. We cite examples of measures adopted by a range of countries, but especially jurisdictions that have, thus far, achieved low numbers of COVID-19 deaths and limited community transmission of severe acute respiratory syndrome coronavirus 2. Finally, we highlight gaps in knowledge where research should be undertaken. As countries consider long-term measures, there is an opportunity to learn, improve the response and prepare for future pandemics.
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How this classification was reachedexpand
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 | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".