What do we know about people who kill themselves: A trajectory for prevention in Developing Countries.
Bibliographic record
Abstract
Abstract: Lecture Title: What do we know about people who kill themselves: A trajectory for prevention in Developing Countries. Amresh Shrivastava 1, Megan Johnston 2 Address: 1. Department of Psychiatry, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada); Mental Health Foundation of India (PRERANA Charitable Trust) 209 Shivkripa Complex, Gokhale Road, Thane, Mumbai, Maharashtra, India 400 602 (Present Address: Regional Mental Health Care, 467 Sunset Drive, St. Thomas, Ontario, Canada N5H 3V9; 2. Department of Psychology, University of Toronto. About one million people die due to suicide every year. and five to Six million make an attempt .Needless to say that suicide is grossly under reported and under recognized. Sitigma of suicide and stigma of mental illness, continues to be a major barrier in the pathway of identification, intervention , treatment and prevention [1,2].Suicide prevention strategies are culturally and geographically driven. The common strategies are to address underlying mental disorders and psychosocial stressful situations. Developing countries have more that two-third share of suicide in the world. Understanding the problem of suicide in context of mental illness needs to change because it appears ill conceived. Recent data continues to support increasing role of non-disease or No Axis –I factors. It lays emphasis on changing concept of psychiatric diagnosis from categorical approach to dimensional approach. It explains that individuals who remain at risk may not have a mental illness and they still have high rates and risk of suicide. [3,4,5]Current biological research also shows that traditional and well known risk factors have their roots of origin in several socio-ecological factors e.g. abuse, trauma, inadequate parenting, religious and spiritual beliefs. [7,8,9]We propose that vision for prevention needs a paradigm shift to focus on psychosocial factors, risk situations, quality of life. It should address the issues like marginalization and social equity rather than to continue to project a tunnel vision of mental illness. This only adds to further stigma. Education can make a difference. More research is required in biopsychosocial model of trajectory of suicide and for pathways of prevention. References: 1. National Crime records Beauru Ministry of Homes, , Government of India, 2007 2. Wold Health organization, 2009 3. Shrivastava Amresh, Johnston Megan, Mitta S.D. Moyo.P. A study of ‘No Axis –I diagnosis’ in admitted Psychiatric patients in United Kindgom, A clinical Audit. (unpublished) 2009. 4. Shaffer D, Gould MS, Fisher P, Trautman P, Moreau D, Kleinman M, Flory M. Arch Gen Psychiatry. 1996 Apr;53(4):339-48. 5. Parkar SR, Dawani V, Weiss MG. Cult Med Psychiatry. 2008 Dec;32(4):492-515. 6. Cad Saude Publica. 2009 Sep;25(9):2064-74 7. Psychiatr Serv. 2009 Aug;60(8):1135-8. 8. June A, Segal DL, Coolidge FL, Klebe K.Aging Ment Health. 2009 Sep;13(5):753-60. 9. Robinson J Prevalence and predictors of suicide attempt in an incidence cohort of 661 young people with first-episode psychosis., Aust N Z J Psychiatry. 2009 Feb;43(2):149-57
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".