Feasibility of a universal suicidality tool for adolescents
Bibliographic record
Abstract
BACKGROUND: The suicide rate among adolescents has been increasing rapidly over the past several years. LOCAL PROBLEM: Adequate screening for suicide risk in this population, particularly youth of color, is lacking. METHODS: The Ask Suicide-Screening Questions (ASQ) tool was implemented at two adolescent-focused health clinics in a large U.S. city. INTERVENTIONS: This project followed the Ottawa Model of Research Use. Participating clinicians were surveyed before and after receiving an educational module on suicide risk screening, the ASQ tool, and clinical pathways. Clinicians were also asked about the feasibility and acceptability of the ASQ tool in their practice. An electronic medical records software was used to gather data on patients newly screened for suicide risk using the ASQ tool. RESULTS: Among eligible patients, 40.2% were screened using the ASQ tool during the 4-month duration of the project. Most clinicians reported that using the tool was feasible within their practice (66%) and 100% endorsed its acceptability (i.e., reporting that they were comfortable screening for suicide and that the ASQ was easy to use). CONCLUSIONS: The ASQ may be a promising screening tool for clinicians to use to address the mental health needs of at-risk youth. This project supports the universal acceptability and feasibility of its use in inner-city primary care clinics.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".