Applying Behavioral Economics to Enhance Safe Firearm Storage
Bibliographic record
Abstract
Behavioral economics applies key principles from psychology and economics to address obstacles to behavior change. The important topic of pediatric firearm injuries has not yet been explored through a behavioral economic lens. Pediatric firearm-related injuries are a significant public health problem in the United States. Despite American Academy of Pediatrics guidelines advising that firearms be stored unloaded, in a locked box or with a locking device, and separate from ammunition, estimates suggest that ∼4.6 million children live in homes with at least 1 loaded and unlocked firearm. In this article, we use behavioral economic theory to identify specific cognitive biases (ie, present bias; in-group, out-group bias; and the availability heuristic) that may influence parental decision-making around firearm storage. We illustrate situations in which these biases may occur and highlight implementation prompts, in-group messengers, and increased salience as behaviorally informed strategies that may counter these biases and subsequently enhance safe firearm storage. We also describe other opportunities to leverage the behavioral economic tool kit. By better understanding the individual behavioral levers that may impact decision-making around firearm storage, behavioral scientists, pediatric providers, and public health practitioners can partner to design and test tailored interventions aimed at decreasing pediatric firearm injuries. Further empirical study is warranted to identify the presence of specific biases and heuristics and determine the most effective behavior change strategies for different subpopulations.
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.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".