Prevalence and Correlates of Youth Suicidal Ideation and Attempts: Evidence from the 2014 Ontario Child Health Study
Bibliographic record
Abstract
OBJECTIVES: To present the 12-month prevalence and correlates of suicidal ideation and attempts in a sample of youth in Ontario. METHODS: Data come from the 2014 Ontario Child Health Study, a provincially representative survey of families with children in Ontario. Youth aged 14 to 17 y ( n = 2,396) completed a computer-assisted, self-administered questionnaire in their home to assess the occurrence of suicidal ideation, suicidal attempts, and associated correlates, including non-suicidal self-injury, mental disorders, substance use, peer victimization and exposure to child maltreatment. Socio-demographic information was collected from the parent. Logistic regression models were used to identify correlates that distinguished between youth reporting: 1) no suicidal ideation or attempts, 2) suicidal ideation but no attempts, and 3) suicidal ideation and attempts. RESULTS: The 12-month prevalence of suicidal ideation and attempts was 8.1% and 4.3%, respectively. All clinical and behavioural correlates were significantly higher among youth reporting suicidal ideation or attempts, as compared with non-suicidal youth. In adjusted models, depression and non-suicidal self-injury were each independently associated with elevated odds of suicidal ideation (OR = 4.84 and 4.19, respectively) and suicidal attempt (OR = 7.84 and 22.72, respectively). Among youth who reported suicidal ideation, the only variable that differentiated youth who attempted suicide v. those who did not, in adjusted models, was non-suicidal self-injury (OR = 3.89). CONCLUSIONS: Suicidal ideation and attempts are common among youth in Ontario, often co-occurring with mental disorders and high-risk behaviours. These findings underscore the need for effective prevention and intervention strategies, particularly for youth depression and non-suicidal self-injury.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".