Brain tissue is needed to further knowledge about underlying biological mechanism of neurodegenerative diseases, however it is a sensitive topic. Materials assist with family discussion and facilitate the family's follow-through with BD.

Objective: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state. Methods: SCI data were collected from high-risk psychiatric inpatients (N = 591) grouped based on their short-term suicidal behavior, that is, those who attempted suicide between intake and 1-month follow-up dates (N = 20) and those who did not (N = 571). Data were analyzed using three predictive algorithms (logistic regression, random forest, and gradient boosting) and three sampling approaches (split sample, Synthetic minority oversampling technique, and enhanced bootstrap). Results: The enhanced bootstrap approach considerably outperformed the other sampling approaches, with random forest (98.0% precision; 33.9% recall; 71.0% Area under the precision-recall curve [AUPRC]; and 87.8% Area under the receiver operating characteristic [AUROC]) and gradient boosting (94.0% precision; 48.9% recall; 70.5% AUPRC; and 89.4% AUROC) algorithms performing best in predicting positive cases of near-term suicidal behavior using this dataset. Conclusions: ML can be useful in analyzing data from psychometric scales, such as the SCI, and for predicting near-term suicidal behavior. However, in cases such as the current analysis where the data are highly imbalanced, the optimal method of measuring performance must be carefully considered and selected.

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Objective Clinicians’ negative emotional responses to suicidal patients are predictive of near‐term suicidality. This study aimed to explore the underlying pathway of this association by investigating the potential relationship between clinicians’ emotional responses and the Narrative Crisis Model of suicide, which comprises long‐term risk factors (LTRF) of suicidal thoughts and behaviors, Suicidal Narrative, and the Suicide Crisis Syndrome (SCS), a presuicidal affective state. Method One thousand and One patient participants and 169 clinician participants were recruited. Patients’ Suicidal Narrative, SCS, and LTRF were assessed at intake using the Suicidal Narrative Inventory (SNI), the Suicide Crisis Inventory, and a composite score of three separate scales, respectively. Clinicians’ emotional responses were measured immediately after patient intake using the Therapist Response Questionnaire‐Suicide Form (TRQ‐SF). Results Multilevel regression analyses, which controlled for clinician differences, found that only patients’ SNI total score and perceived burdensomeness subscale score were significantly associated with clinicians’ TRQ‐SF total score. Furthermore, a higher SNI total score was significantly related to higher distress and lower affiliation scores among clinicians. Conclusions Clinicians appear to respond emotionally to patients’ Suicidal Narrative, and thus, future investigation of Suicidal Narrative and its potential to improve imminent suicide risk assessment is warranted.

Objective: This study examines how clinicians' emotional responses to suicidal patients and their emotion regulation abilities are related to their treatment recommendations for these patients and to patients' concurrent suicidal ideation and at one-month follow-up. Methods: Adult psychiatric outpatients (N = 361) and the mental health professionals evaluating them for treatment (N = 43) completed self-report assessments following their first clinical meeting. Clinician emotion regulation traits, emotional responses to individual patients, and the recommended intensity of treatment were assessed. Patients were assessed for suicidal ideation immediately following the initial meeting and at a one-month follow-up. Moderation and mediation analyses were performed to examine the relationships between study variables. Results: Patient suicidal ideation at the initial clinical encounter was associated with increased negative emotions in clinicians with lower emotion regulation. Further, recommended treatment intensity was associated with clinicians' negative emotional responses but not with patient suicidal ideation among clinicians with lower emotion regulation. Conclusions: Treatment intensification is related to clinicians' emotion regulation abilities. Clinicians' attention to their emotional responses may facilitate improved treatment process and ultimately may improve suicidal outcomes.