Collateral Crises of Gun Preparation and the COVID-19 Pandemic: Infodemiology Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In the past, national emergencies in the United States have resulted in increased gun preparation (ie, purchasing new guns or removing guns from storage); in turn, these gun actions have effected increases in firearm injuries and deaths. OBJECTIVE: The aim of this paper was to assess the extent to which interest in gun preparation has increased amid the coronavirus disease (COVID-19) pandemic using data from Google searches related to purchasing and cleaning guns. METHODS: We fit an Autoregressive Integrated Moving Average (ARIMA) model over Google search data from January 2004 up to the week that US President Donald Trump declared COVID-19 a national emergency. We used this model to forecast Google search volumes, creating a counterfactual of the number of gun preparation searches we would expect if the COVID-19 pandemic had not occurred, and reported observed deviations from this counterfactual. RESULTS: Google searches related to preparing guns have surged to unprecedented levels, approximately 40% higher than previously reported spikes following the Sandy Hook, CT and Parkland, FL shootings and 158% (95% CI 73-270) greater than would be expected if the COVID-19 pandemic had not occurred. In absolute terms, approximately 2.1 million searches related to gun preparation were performed over just 34 days. States severely affected by COVID-19 appear to have some of the greatest increases in the number of searches. CONCLUSIONS: Our results corroborate media reports that gun purchases are increasing amid the COVID-19 pandemic and provide more precise geographic and temporal trends. Policy makers should invest in disseminating evidence-based educational tools about gun risks and safety procedures to avert a collateral public health crisis.
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.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it