Risk Assessment of Suicidality: Bridging the Gap Between Clinical Practices & Requirements for Patient’s Safety
Bibliographic record
Abstract
Patients safety is of Paramount concern in clinical practice and administrative psychiatry. There is evidence of limitations in assessment of suicide for patients coming to services. Treatment of mental disorder in universally advocated for prevention of suicide as up to 90% suicides arise from mental illnesses. It is therefore important that patients who seek services are well looked after. Suicide behavior and suicidal ideation are with considerable risk for attempt of suicide. Suicidal ideation is common in about 4% in general population, in about 20% of psychiatric population & in about 60-70% of admissions in acute psychiatric wards. There are few tools available for risk assessment of such patients & almost always this is done based upon personal clinical judgment. The science of suicidology is constantly evolving with changing socio-cultural perspectives. Much more research is required in making judgments for suicidality. Available literature suggests three main domains for origin of suicidal ideas i.e. Biological domain, Psychological domain and Social-Environmental Domain. The suicidal ideas have constant interplay with risk factors present in the individual to give rise to suicidal thoughts, which become morbid. The cognitive set changes and cognitive control is lost which gives rise to an attempt. For an adequate risk assessment one needs to take into consideration the three domains and known risk factors against the background of suicide protectors. The paper discusses findings of a new tool: SIS-MAP (Scale for Impact of Suicidality: management, Assessment & planning of care) with objective to improvise assessment and patient's safety.
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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 | 0.059 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".