Attentional bias toward suicide-relevant information in suicide attempters: A cross-sectional study and a meta-analysis
Bibliographic record
Abstract
Objective Previous studies using a modified Stroop test suggested that suicide attempters, in contrast to depressed patients with no suicidal history, display a particular attentional bias toward suicide-related cues. However, negative results have also been reported. In the present study, we collected new data and pooled them as part of a meta-analysis intended to shed further light on this question. Method We conducted: – a cross-sectional study comparing performance on the modified Stroop task for suicide-related, positively-valenced and negatively-valenced words in 33 suicide attempters and 46 patient controls with a history of mood disorders; – a systematic review and a meta-analysis of studies comparing performance on the modified Stroop task among patients with vs. without a history of suicidal acts in mood disorders. Results The cross-sectional study showed no significant difference in interference scores for any type of words between suicide attempters and patient controls. A meta-analysis of four studies, including 233 suicide attempters and 768 patient controls, showed a significant but small attentional bias toward suicide-related words (Hedges’g = 0.22; 95% CI [0.06 to 0.38]; Z = 2.73; P = 0.006), but not negatively-valenced words (Hedges’g = 0.06; 95% CI [−0.09 to 0.22]; Z = 0.77; P = 0.4) in suicide attempters compared to patient controls. Limitations Positively-valenced words and healthy controls could not be assessed in the meta-analysis. Conclusion Our data support a selective information-processing bias among suicide attempters. Indirect evidence suggests that this effect would be state-related and may be a cognitive component of the suicidal crisis. However, we could not conclude about the clinical utility of this Stroop version at this stage. Disclosure of interest The authors have not supplied their declaration of competing interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".