RISK ASSESSMENT OF SUICIDE IN CLINICAL PRACTICE
Bibliographic record
Abstract
Suicide is a global public health problem. Its management in clinical practice is complex and challenging .Studies show about 26% suicide in mental health system. Out of these, 14% commit suicide during hospital stay; about 50 - 90% have at least one psychiatric diagnosis. 60 - 70% of patients are hospitalized due to an attempt or potential crisis, about 15 - 20% attempt suicide prior to admission. Suicide is also common in post-discharge period. Every psychiatrist on an average loses atleast on client due to suicide in an average span of 20 years of practice. In about 70% of cases, suicide behavior is there as on for hospitalization in acute settings. Continuous training and skill development are two of the most important measures in clinical practice for dealing with suicide behavior. High suicide rates are reported in prodromal stage, acute illness, post-hospitalization and soon after discharge in the community. A clinician faces challenging situations while determining the level of care and referral for a patient with a high suicide potential. There is continued struggle amongst clinicians for decision- making in regards to the need for hospitalization, level of monitoring, voluntary status, and time of discharge. It is generally agreed that suicide is difficult to predict and prevent; however, in order to develop clinical excellence and offer a standard of care, continued education and knowledge translation for bringing research into practice is the least that can be done. Inspite of this need, continued education for mental health professionals and psychiatrists in-training remains limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".