How many democratic countries have conducted COVID-19 public inquiries? An exploratory study of government-led postpandemic reviews (2020–2024)
Bibliographic record
Abstract
Introduction: Many governments have initiated national inquiries into their responses to the COVID-19 pandemic. Lessons drawn from them will matter for public health policies. While these inquiries represent an opportunity for policy learning, there may also be obstacles. This study helps to explore these opportunities and obstacles by providing an initial survey of COVID-19 inquiries. Methods: We collected a novel data set of national COVID-19 inquiries in democratic countries, taking note of their type, membership, timing, mandate and whether their terms of reference asked the inquiry to consider the adequacy of the government pandemic response as well as the collateral harms arising from government interventions. We conducted a series of panel logit analyses to examine the extent to which country-level factors-such as the level of democracy and executive oversight, centralisation of executive power and economic development-were associated with the likelihood of appointing a COVID-19 inquiry. Results: We found 32 national COVID-19 inquiries, held in 25 (32%) of the countries in our data set, which included 78 countries with a score of at least 0.6 on the 2019 Varieties of Democracy (V-Dem) Electoral Democracy Index. Of the 32 national inquiries, 14 (44%) were public inquiries (proper), 15 (47%) were inquiries conducted by parliamentary committees and 3 (9%) were another type of inquiry. The earliest public inquiries (proper) were launched in the first half of 2020 in the Scandinavian countries. Generally, countries were slightly quicker to establish parliamentary committee inquiries than public inquiries proper. Many democracies, such as Canada, have yet to initiate one at all.A country's probability of initiating a COVID-19 inquiry was positively correlated with its level of democracy, gross domestic product per capita and executive oversight, but negatively correlated with higher values of the V-Dem index of presidentialism. These correlations were significant once we controlled for multicollinearity. The vast majority of inquiries (77%) were appointed in 2020 and 2021. Most inquiries' terms of reference were relatively open-ended, with few specifically demanding an examination of policy adequacy and most urging some sort of investigation into the COVID-19 measures' collateral harms. Conclusion: Although slightly less than a third of countries in our sample have initiated inquiries into their COVID-19 response, those that have tend to mention collateral harms in their terms of reference, but not policy inadequacy. Our exploratory study should be followed by fine-grained textual analyses of individual inquiries.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".