Integration of a suicide risk assessment and intervention approach: the perspective of youth
Bibliographic record
Abstract
The process of suicide risk assessment is often a challenge for mental health nurses, especially when working with an adolescent population. Adolescents who are struggling with particular problems, stressors and life events may exhibit challenging and self-harm behaviour as a means of communication or a way of coping. Current literature provides limited exploration of the effects of loss, separation and divorce, blended families, conflict and abuse on child and adolescent development and the increased vulnerability of at-risk youth. There is also limited research that provides clear and practical models for the assessment and management of youth suicidal ideation and behaviour. This paper will discuss the integration of a number of theories to establish a comprehensive assessment of risk. The research study described the perspective of youth and their families who had experienced this particular model; however, this paper will discuss only the youth perspective. In order for this model to be successful, it is important for mental health nurses to make a connection with the youth and begin to understand the self-harm behaviour in context of the adolescents' family, and their social and school experiences. It also requires recognition that adolescents with challenging and self-harm behaviour are hurting and troubled adolescents with hurtful and troublesome behaviour.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".