Public participation in decisions about measures to manage the COVID-19 pandemic: a systematic review
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, governments and health authorities faced tough decisions about infection prevention and control measures such as social distancing, face masks and travel. Judgements underlying those decisions require democratic input, as well as expert input. The aim of this review is to inform decisions about how best to achieve public participation in decisions about public health and social interventions in the context of a pandemic or other public health emergencies. OBJECTIVES: To systematically review examples of public participation in decisions by governments and health authorities about how to control the COVID-19 pandemic. DESIGN: We searched Participedia and relevant databases in August 2022. Two authors reviewed titles and abstracts and one author screened publications promoted to full text. One author extracted data from included reports using a standard data-extraction form. A second author checked 10% of the extraction forms. We conducted a structured synthesis using framework analysis. RESULTS: We included 24 reports (18 from Participedia). Most took place in high-income countries (n=23), involved 'consulting' the public (n=17) and involved public meetings (usually online). Two initiatives reported explicit support for critical thinking. 11 initiatives were formally evaluated (only three reported impacts). Many initiatives did not contribute to a decision, and 17 initiatives did not include any explicit decision-making criteria. CONCLUSIONS: Decisions about how to manage the COVID-19 pandemic affected nearly everyone. While public participation in those decisions had the potential to improve the quality of the judgements and decisions that were made, build trust, improve adherence and help ensure transparency and accountability, few examples of such initiatives have been reported and most of those have not been formally evaluated. Identified initiatives did point out potential good practices related to online engagement, crowdsourcing and addressing potential power imbalance. Future research should address improved reporting of initiatives, explicit decision-making criteria, support for critical thinking, engagement of marginalised groups and decision-makers and communication with the public. PROSPERO REGISTRATION NUMBER: 358991.
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 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".