Intentions to use mental health and suicide prevention resources among individuals with symptoms of the suicide crisis syndrome and/or suicidal ideation
Bibliographic record
Abstract
INTRODUCTION: The suicide crisis syndrome (SCS) has demonstrated efficacy in predicting suicide attempts, showing potential utility in detecting at-risk individuals who may not be willing to disclose suicidal ideation (SI). The present international study examined differences in intentions to utilize mental health and suicide prevention resources among community-based adults with varying suicide risk (i.e., presence/absence of SCS and/or SI). METHODS: A sample of 16,934 community-based adults from 13 countries completed measures about the SCS and SI. Mental health and suicide prevention resources were provided to all participants, who indicated their intentions to use these resources. RESULTS: Individuals with SCS (55.7%) were just as likely as those with SI alone (54.0%), and more likely than those with no suicide-related symptoms (45.7%), to report willingness to utilize mental health resources. Those with SI (both with and without SCS) were more likely to seek suicide prevention resources (52.6% and 50.5%, respectively) than those without SI (41.7% and 41.8%); however, when examining endorsements for personal use, those with SCS (21.6%) were more likely to use resources than individuals not at risk (15.1%). CONCLUSIONS: These findings provide insight into individuals' willingness to use resources across configurations of explicitly disclosed (SI) and indirect (SCS) suicide risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".