Assessment of self-harm risk using implicit thoughts.
Bibliographic record
Abstract
Assessing for the risk of self-harm in acute care is a difficult task, and more information on pertinent risk factors is needed to inform clinical practice. This study examined the relationship of 6 forms of implicit cognition about death, suicide, and self-harm with the occurrence of self-harm in the future. We then attempted to develop a model using these measures of implicit cognition along with other psychometric tests and clinical risk factors. We conducted a prospective cohort of 107 patients (age > 17 years) with a baseline assessment that included 6 implicit association tests that assessed thoughts of death, suicide, and self-harm. Psychometric questionnaires were also completed by the patients, and these included the Beck Hopelessness Scale (Beck, Weissman, Lester, & Trexler, 1974), Barratt impulsiveness scale (Patton, Stanford, & Barratt, 1995), brief symptom inventory (Derogatis & Melisaratos, 1983), CAGE questionnaire for alcoholism (Ewing, 1984), and the drug abuse screening test 10 (Skinner, 1982). Medical and demographic information was also obtained for patients as potential confounders or useful covariables. The outcome measure was the occurrence of self-harm within 3 months. Implicit associations with death versus life as a predictor added significantly (odds ratio = 5.1, 95% confidence interval [1.3, 20.3]) to a multivariable model. The model had 96.6% sensitivity and 53.9% specificity with a high cutoff, or 58.6% sensitivity and 96.2% specificity with a low cutoff. This scale shows promise for screening emergency department patients with mental health presentations who may be at risk for future self-harm or suicide.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".