Suicide Ideation Severity and Oculomotor Avoidance of Suicide‐Related Stimuli
Bibliographic record
Abstract
INTRODUCTION: Identifying variables linked to distinct suicide outcomes has long been among suicidology's research priorities. Cognitive theories of suicide identify attentional processes that may vary for individuals at a greater suicide risk. However, an overreliance on self-report and objective measures that are poor estimates of attention has led to mixed findings. METHOD: The current study utilizes eye tracking with a novel passive viewing task to explore differences in viewing patterns for suicide- and neutral-image pairings, as a function of suicide outcomes. Young adults (N = 124, 83.9% women) were oversampled for recent suicidal thoughts and behaviors, and completed a series of self-report questionnaires specific to suicide history, as well as relevant covariates prior to completing the eye-tracking task. RESULTS: Multilevel modeling revealed that individuals with low-to-moderate ratings of past-year ideation displayed a significant decline in the amount of time spent viewing suicide images as compared to neutral images over the course of the task (oculomotor avoidance). However, the same pattern was not seen among individuals with high suicide ideation, specifically those with resolved plans and preparation. Furthermore, no differences were found between people with and without a suicide attempt history. CONCLUSION: These results suggest a suicide-specific disengagement bias among individuals high in suicide ideation and more specifically, resolved plans and preparation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".