Do associations between suicide ideation and its correlates (substance use, anxiety, and depression) differ according to victimization type among youth? A Smart platform study
Bibliographic record
Abstract
The issues associated with mental health, substance misuse, and suicide ideation are complex and sensitive among youth. We sought to investigate the role that subjective health, internalizing and externalizing risk factors play in the association between victimization and suicide ideation among youth in Canada via used a custom-built digital epidemiological smartphone application (Smart Platform) on their personal smartphones. A sample of 818 youth citizen scientists in Saskatchewan, Canada downloaded the app to provide information on victimization, subjective health, internalizing problems (symptoms of stress, anxiety, and depression), externalizing behaviours (cannabis use, alcohol, smoking), and suicide ideation. Binary regression models were used to estimate associations and controlled for gender, age, perpetration, and ethnicity. From our sample, 23% of youth reported suicide ideation (i.e., thoughts) in the past year. Three types of victimization (cyberbullied, made fun or teased, or bullied via being left out) are associated with a two-times higher risk of suicide ideation. Although certain risk factors (anxiety, poor subjective health, and cannabis use) were associated with higher suicide ideation risk, they did not moderate the association between victimization and suicide ideation. Symptoms of depression were found to be protective against suicide ideation. Suicide ideation is high among this sample of youth in Canada. Certain types of victimization, internalizing and externalizing risk factors, and poor subjective health are associated with a higher risk of suicide ideation. However, our findings confirm that the pathway from victimization to suicide ideation is complex and is potentially moderated by factors other than the ones explored here.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".