Public participation in decisions about public health and social measures used to manage the Covid-19 pandemic: protocol for a systematic review
Bibliographic record
Abstract
Background During the Covid-19 pandemic, governments and health authorities have had to make difficult decisions about infection prevention and control measures such as social distancing, face masks, travel restrictions, self-isolation, quarantines, lockdowns, and vaccination. Judgments underlying those decisions require democratic input, as well as expert input. Objective To systematically review examples of participatory democracy in decisions about how to control the Covid-19 pandemic, including the level or degree of participation, inclusion of marginalised groups, the methods used for participation, the decision-making process, support for understanding and applying relevant concepts for making well-informed decisions, and evaluation of the effects of participation. Methods We will include reports that describe public participation in deliberations or decisions by a government, public health authority, or international agency about infection prevention and control measures during the Covid-19 pandemic, including case reports and any type of qualitative or quantitative evaluation. We will search Participedia, MEDLINE, Embase, IPSA, Sociological Abstracts, ProQuest Politics Collection, CPSI-S, CPSI-SSH, Web of Science Core Collection, Clarivate Analytics, Citationchaser, and relevant websites, including Open Government Partnership, the International Observatory on Participatory Democracy, and Involve. Two review authors will independently read the titles and abstracts resulting from the search process and eliminate any obviously irrelevant reports. We will retrieve the full text of potentially relevant reports. Two review authors will then assess each retrieved report for inclusion. One review author will extract data from each included report using a standard data-extraction form. A second review author will check the extracted data against the full report. If possible and warranted, we will contact report authors to collect information that is missing from reports. For formal evaluations that report effects of participation on an outcome), we will use the “Risk of Bias In Non-randomised Studies - of Interventions” (ROBINS-I) tool to assess the risk of bias. We will undertake a structured synthesis.
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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 | 0.143 | 0.172 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.021 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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 source (direct Gemma or distilled Codex), 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".