Emergency Department Assessment of Self-Harm Risk Using Psychometric Questionnaires
Bibliographic record
Abstract
OBJECTIVE: To examine several potential predictive screening tools for emergency department assessment of risk of self-harm. METHODS: Adult patients presenting with self-harm or suicidal ideation were enrolled at 2 emergency departments at large teaching hospitals in Edmonton, Alberta. Patients completed a brief interview assessing demographics and psychiatric history and several questionnaires (the Beck Hopelessness Scale, the Barrett Impulsiveness Scale [BIS], and the Brief Symptom Inventory [BSI]) and drug and alcohol abuse screens (Drug Abuse Screening Test [DAST-10] and the Cut down, Annoyed, Guilt, Eye-opener [commonly referred to as CAGE] Questionnaire). At 3 months, patients were followed up via telephone and electronic health records to ascertain self-harm outcome. Questionnaires and their subscales were assessed using logistic regression. Receiver operating characteristic (ROC) analysis was performed on the results. RESULTS: Among the 157 patients enrolled, 49% were women and 36% (of the total) were aged 18 to 29 years. Several of the subscales of the BSI and BIS as well as the DAST-10 were significant predictors of self-harm (P < 0.05). ROC analysis showed that none of the scales in isolation were very strong predictors. Hierarchical regression analysis that combined the significant scales with clinical risk factors that were significantly related to self-harm (that is, age, education level, history of self-harm, and whether they presented with self-harm or only suicidal ideation) indicated that the BIS and DAST-10 questionnaires each added significantly to the predictive ability of a model with these risk factors. CONCLUSIONS: While many of the questionnaires and their related constructs are related to future self-harm, none of them are particularly strong and their diagnostic usefulness is limited.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".