Bibliographic record
Abstract
Management of suicide behavior in psychiatric practice Amresh Shrivastava1 Running Title: Affiliations: 1 Department of Psychiatry, Associate Professor of Psychiatry and The University of Western Ontario, Associate Scientist, Lawson Health Research Institute London, ON, Canada, N6A 5C1 Correspondence: Regional Mental Health Care, 467 Sunset Drives, St. Thomas, Ontario, N5H 3V9, the University of Western Ontario, London, Canada E-mail: Amresh Srivastava* - dr.amresh@gmail.com\nSuicide is a global public health problem, and its management in clinical practice remains complex and challenging. Studies show that about 26% of suicides are found within the mental health system. Out of these, 14% commit suicide during hospitalization.[1] Further, about 50-90% have at least one psychiatric diagnosis;[2] 60-70% patients are hospitalized due to an attempt or potential crisis;[3] and about 15-20% of patients attempt suicide prior to admission, while also being common in post-discharge periods.[4, 5] There is a widespread struggle between clinicians with decision making in regards to the need for hospitalization, level of monitoring, voluntary status, and time to discharge. It is generally agreed that suicide is difficult to predict and prevent; however, in order to develop clinical excellence, it seems that continued education and knowledge translation for bringing research into practice is the least that can be done. In spite of this need, continued education for mental health professionals and psychiatrists in-training remains limited. The present course is unique because (1) it addresses a common day-to-day problem, and (2) it offers a core curriculum to buildup the competency of clinicians. This course offers education for qualitative risk assessment, skill building for identification of suicidality, pharmacological and psychosocial management, as well as care planning and implementation of preventive strategies in order to enhance the clinician’s confidence within an inpatient, ambulatory, and community setting. This course will be conducted using didactic short lectures, case discussion, hands-on training, audio-visual learning, group discussion, and self-assessment. Furthermore, clinicians will be evaluated for meeting the objectives, relevance, content and quality, using a structured assessment format. References 1.\tHunt IM, Windfuhr K, Swinson N, Shaw J, Appleby L, Kapur N. Suicide amongst psychiatric in-patients who abscond from the ward: a national clinical survey. BMC Psychiatry 2010;10(14). 2.\tNordentoft M, Madsen T. High risk of suicide among psychiatric patients. Ugeskr Laeger 2011;173(39):2415-8. 3.\tMiret M, Nuevo R, Morant C, Sainz-Cortón E, Jiménez-Arriero MÁ, López-Ibor JJ et al. The role of suicide risk in the decision for psychiatric hospitalization after a suicide attempt. Crisis 2011;32(2):65-73. 4.\tOiesvold T, Bakkejord T, Hansen V, Nivison M, Sørgaard KW. Suicidality related to first-time admissions to psychiatric hospital. Soc Psychiatry Psychiatr Epidemiol. 2011;Epub ahead of print. 5.\tAjdacic-Gross V, Lauber C, Baumgartner M, Malti T, Rössler W. In-patient suicide--a 13-year assessment. Acta Psychiatr Scand 2009;120(1):71-5.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".