Suicides Before, During, and After Daylight Savings Time in the United States
Bibliographic record
Abstract
This study was designed to investigate differences in the number of suicides committed in the United States before, during, and after daylight savings time (DST). Conflicting results in the literature suggest both a positive and negative effect of DST in the physical, mental, behavioral aspects society. As a result, some states are proposing legislation to abolish DST while others are trying to make DST permanent. This study is designed to investigate whether DST has a positive negative, or no effect on the frequency of suicide. Archival data from a governmental public database containing the total number of suicides by year and month from 2000-2017 was used. Daylight savings time was defined as the months of March through October while non-DST consisted of the remaining 4 months. The data were organized into 3 groups of 4 months beginning in November, 2007 and ending in October, 2017. The results demonstrated a statistically significant increase in suicides during DST. Most suicides were committed during July-October (M = 74.69, SD = 68.86), compared to March-June (M = 73.56, SD = 67.89), and November-February (M = 67.00, SD = 61.41). Despite disagreement in the literature, this study would suggest eliminating DST altogether. These results support other evidence which suggest a detrimental effect of DST, especially with respect to the psychological and behavioral aspects of public health. Nevertheless, there is still a need for more research to determine the impact of these one hour time shifts in the Spring and Fall.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".