Impacts of school shooter drills on the psychological well-being of American K-12 school communities: a social media 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
Abstract The toll from gun violence in American K-12 schools has escalated over the past 20 years. School administrators face pressure to prepare for possible active shootings, and often do so through drills, which can range from general lockdowns to simulations, involving masked “shooters” and simulated gunfire, and many variations in between. However, the broad and lasting impact of these drills on the well-being of school communities is poorly understood. To that end, this article applies machine learning and interrupted time series analysis to 54 million social media posts, both pre- and post-drills in 114 schools spanning 33 states. Drill dates and locations were identified via a survey, then posts were captured by geo-location, school social media following, and/or school social media group membership. Results indicate that anxiety, stress, and depression increased by 39–42% following the drills, but this was accompanied by increases in civic engagement (10–106%). This research, paired with the lack of strong evidence that drills save lives, suggests that proactive school safety strategies may be both more effective, and less detrimental to mental health, than drills.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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