The unintended consequences of COVID-19 mitigation measures matter: practical guidance for investigating them
Bibliographic record
Abstract
Abstract Background COVID-19 has led to the adoption of unprecedented mitigation measures which could trigger many unintended consequences. These unintended consequences can be far-reaching and just as important as the intended ones. The World Health Organization identified the assessment of unintended consequences of COVID-19 mitigation measures as a top priority. Thus far, however, their systematic assessment has been neglected due to the inattention of researchers as well as the lack of training and practical tools. Main text Over six years our team has gained extensive experience conducting research on the unintended consequences of complex health interventions. Through a reflexive process, we developed insights that can be useful for researchers in this area. Our analysis is based on key literature and lessons learned reflexively in conducting multi-site and multi-method studies on unintended consequences. Here we present practical guidance for researchers wishing to assess the unintended consequences of COVID-19 mitigation measures. To ensure resource allocation, protocols should include research questions regarding unintended consequences at the outset. Social science theories and frameworks are available to help assess unintended consequences. To determine which changes are unintended, researchers must first understand the intervention theory. To facilitate data collection, researchers can begin by forecasting potential unintended consequences through literature reviews and discussions with stakeholders. Including desirable and neutral unintended consequences in the scope of study can help minimize the negative bias reported in the literature. Exploratory methods can be powerful tools to capture data on the unintended consequences that were unforeseen by researchers. We recommend researchers cast a wide net by inquiring about different aspects of the mitigation measures. Some unintended consequences may only be observable in subsequent years, so longitudinal approaches may be useful. An equity lens is necessary to assess how mitigation measures may unintentionally increase disparities. Finally, stakeholders can help validate the classification of consequences as intended or unintended. Conclusion Studying the unintended consequences of COVID-19 mitigation measures is not only possible but also necessary to assess their overall value. The practical guidance presented will help program planners and evaluators gain a more comprehensive understanding of unintended consequences to refine mitigation measures.
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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 | 0.273 | 0.506 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.020 | 0.043 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".