Nocturnal Wakefulness and Suicide Risk in the Australian Population
Bibliographic record
Abstract
Objective: Temporal patterns for suicide over a 24-hour period have shown mixed results among prior studies. However, analyses of 24-hour temporal patterns for wakeful actions including suicidal behavior should adjust for expected sleep requirements that inherently skew such activities to conventional wakeful times. This study analyzed the time-of-day for suicide cases from the Australian population for the year 2017, adjusting for expected sleep patterns. Identification of time-of-day trends using this methodology may reveal risk factors for suicide and potentially modifiable contributors. Methods: The Australian National Coronial Information System database was accessed, and data for completed suicide were extracted for the most recent completed year (2017). Time of suicide was allocated to one of four 6-hourly time bins across 24 hours, determined from time last seen alive and time found subsequently. Prevalence of suicide for each time bin was adjusted for the likelihood of being awake for each bin according to sleep-wake norms published from a large Australian community survey. Observed prevalence of suicide was compared to expected values predicted from likelihood of being awake across each time bin calculated as a standardized incidence ratio (SIR). Results: For the year 2017, there were 2,808 suicides, of which 1,417 were able to be allocated into one of four 6-hourly time bins. When compared to expected values, suicides were significantly more likely to occur in the overnight bin (2301–0500; SIR = 3.93, P < .001). Conclusions: Higher-than-expected rates of suicide overnight associated with nocturnal wakefulness may represent a modifiable risk factor for triggering suicide events.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".