The effectiveness and decay of public health policy actions on infection-control behaviour in the general public: Evidence from a low-COVID prevalence jurisdiction
Bibliographic record
Abstract
BACKGROUND: Public health policies designed to influence individuals' infection-control behaviour are a tool for governments to help prevent the spread of disease. Findings on the impacts of policies are mixed and there is limited information on the effects of removing restrictions and how policies impact behavioural trends. METHODS: We use low-acuity emergency department visits from 12 hospitals in New Brunswick, Canada, (January 2017 -October 2021) as a proxy for infection-control behaviour and provide insight into the effects of the COVID-19 virus on a population with a low prevalence of cases. Quasi-experimental techniques (event studies) are applied to estimate the magnitude and persistence of effects of specific events (e.g., policy changes), to control for COVID-19 cases and vaccines, and to explore how the effectiveness of policy changes during the pandemic as more policies are introduced. RESULTS: Initial tightening of restrictions on March 11, 2020 reduced low-acuity emergency department visits by around 60% and reached a minimum after 30 days. Relaxing policies on social gatherings and personal services gradually increased low-acuity emergency department visits by approximately 50% after 44 days. No effects were found from policies lifting all restrictions, and reinstating a state of emergency on July 31, 2021, and September 24, 2021. CONCLUSION: These results suggest that policy interventions are less likely to be effective at influencing infection control behaviour with time and more policies introduced, and that tracking and publicly reporting case numbers can influence infection control behaviour.
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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 | 0.026 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".