The death-implicit association test and suicide attempts: a systematic review and meta-analysis of discriminative and prospective utility
Bibliographic record
Abstract
Suicide risk assessment involves integrating patient disclosure of suicidal ideation and non-specific risk factors such as family history, past suicidal behaviour, and psychiatric symptoms. A death version of the implicit association test (D-IAT) has been developed to provide an objective measure of the degree to which the self is affiliated with life or death. However, this has inconsistently been associated with past and future suicidal behaviour. Here, we systematically review and quantitatively synthesize the literature examining the D-IAT and suicide attempts. We searched psychINFO, Medline, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) from inception until 9 February 2021 to identify publications reporting D-IAT scores and suicide attempts (PROSPERO; CRD42020194394). Using random-effects models, we calculated standardized mean differences (SMD) and odds ratios (ORs) for retrospective suicide attempts. We then calculated ORs for future suicide attempts. ORs were dichotomized using a cutoff of zero representing equipoise between self-association with life and death. Eighteen studies met our inclusion criteria (n = 9551). The pooled SMD revealed higher D-IAT scores in individuals with a history of suicide attempt (SMD = 0.25, 95% CI 0.15 to 0.35); however, subgroup analyses demonstrated heterogeneity with acute care settings having lower effect sizes than community settings. Dichotomized D-IAT scores discriminated those with a history of suicide attempt from those without (OR 1.38 95% CI 1.01 to 1.89) and predicted suicide attempt over a six-month follow-up period (OR 2.99 95% CI 1.45 to 6.18; six studies, n = 781). The D-IAT may have a supplementary role in suicide risk assessment; however, determination of acute suicide risk and related clinical decisions should not be based solely on D-IAT performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".