Examining suicidal desire through the lens of the three-step theory: A cross-sectional and longitudinal investigation in a community sample.
Bibliographic record
Abstract
OBJECTIVE: Advancing evidence-based theories of suicide is critical to improving suicide prevention. The aim of the current study was to investigate suicidal desire through an emerging theory of suicide, the Three-Step Theory (3ST). Specifically, this study investigated the validity and predictive utility of Steps 1 and 2 of the 3ST in a Canadian community sample. METHOD: Participants were 487 adults between the ages of 35 to 90 (M = 59; 64% female, 87% White) who completed self-report measures assessing suicidal ideation and attempt history, hopelessness, psychological pain, and 5 forms of social connectedness at baseline and 6 months later. RESULTS: In support of 3ST predictions, cross-sectional analyses showed (a) the combination of pain and hopelessness strongly correlated with suicidal desire, and (b) connectedness was protective of suicidal desire among those high in pain and hopelessness. Regarding predictive utility, longitudinal analyses showed that pain and hopelessness were strong predictors of future suicidal desire and that connectedness was protective against future suicidal desire. These relationships of pain, hopelessness, and connectedness remained when controlling for baseline suicidal desire. CONCLUSIONS: Results support the validity and predictive utility of 3ST hypotheses related to suicidal desire. Taken together with previous studies, findings suggest that Steps 1 and 2 are useful for conceptualizing suicide risk and prevention. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".