Suicidality in Primary Care, Youth Mental Health Services: Prevalence, Risk Factors and Implications for Practice
Bibliographic record
Abstract
INTRODUCTION: Youth suicide is a concern worldwide, and suicidality-the presence of suicidal ideation or intent-is a critical risk for youth mental health services. This study aimed to determine the prevalence of suicidality in primary care, youth mental health services, along with its correlates and the course of treatment offered to clients. METHODS: Routinely collected data from Australia's headspace National Youth Mental Health Foundation, which has over 160 centres across Australia providing mental health care to young people aged 12-25 years, were analysed for new clients who started and completed their first episode of care between 1 July 2022 and 30 June 2023. This included 30 437 young people/episodes of care and 74 393 occasions of service. RESULTS: Results showed that suicidality was evident in almost one-quarter of young people, although it was rarely reported as a primary presenting issue. When present, it was usually identified at first visit. Those most at risk were young people in unstable accommodation, who identified as LGBTIQA+ or who were indigenous. CONCLUSIONS: The findings show that suicidality should be anticipated in young people presenting to primary care mental health settings, and youth services need to be able to competently deal with suicide risk rather than using this as exclusion criteria.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".