Proportion of Individuals With Past-Year Suicidal Ideation Who Attempted Suicide Over the Past 10 Years in the United States, and the Influence of Age and Sex
Bibliographic record
Abstract
Abstract. Background: In recent years, the rate of death by suicide has been increasing disproportionately among females and young adults in the United States. Presumably this trend has been mirrored by the proportion of individuals with suicidal ideation who attempted suicide. Aim: We aimed to investigate whether the proportion of individuals in the United States with suicidal ideation who attempted suicide differed by age and/or sex, and whether this proportion has increased over time. Method: Individual-level data from the National Survey on Drug Use and Health (NSDUH), 2008–2017, were used to estimate the year-, age category-, and sex-specific proportion of individuals with past-year suicidal ideation who attempted suicide. We then determined whether this proportion differed by age category, sex, and across years using random-effects meta-regression. Overall, age category- and sex-specific proportions across survey years were estimated using random-effects meta-analyses. Results: Although the proportion was found to be significantly higher among females and those aged 18–25 years, it had not significantly increased over the past 10 years. Limitations: Data were self-reported and restricted to past-year suicidal ideation and suicide attempts. Conclusion: The increase in the death by suicide rate in the United States over the past 10 years was not mirrored by the proportion of individuals with past-year suicidal ideation who attempted suicide during this period.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".