Problematic Conclusions regarding Suicide Prevention: A Response to ten Have et al, 2009
Bibliographic record
Abstract
Dear Editor: This letter is in response to the article published in the December 2009 issue of The Canadian Journal of Psychiatry by Dr Margreet ten Have and colleagues.1 We wish to applaud the authors for a sophisticated and thorough analysis that included the strongest risk factors for suicide, suicide attempt, and suicidal ideation, such as previous history of suicidal behaviour, psychiatric disorder, and parental psychiatric history as well as other putative risk factors in a robust population-based sample. Despite the strength of this manuscript, we feel that it has one substantive weakness - its conclusions do not follow from its results. This is unfortunate because the paper has good standing on its own merits. Our concerns are as follows: the authors write that [t]o date, suicide prevention has received too little attention in the literature and should be implemented more proactively in the community.1 p 831 As the authors are likely aware, the literature is replete with studies of suicide prevention - many of which do not support the effectiveness of such programs in reducing death by suicide.2 Further, community-based suicide prevention-based programs in particular have generally not been found effective in preventing death by suicide.3-5 The authors also conclude that [sjuicide prevention should not be restricted to the mental health sector alone. The reason is that people who consider a suicide attempt are unlikely to be in contact with health care professionals.1'11832 A review of 40 studies regarding health services use among people who died by suicide found that most had contact with mental health or general health services in the year preceding death, and that contact was common in the month prior to death, especially among older adults.6 While the authors are correct that focusing on specialty mental health services alone may not be sufficient, ample evidence exists that enhancing the capacity of the wider health care system, either through primary care physician training7'8 or health care linked gatekeeper training9 can be effective in reducing suicide deaths. Thus it is essential that suicide prevention efforts use strategies that we know are most likely to be effective: namely, improving capacity for suicide risk management in mental health services and general health care, rather than strategies that we are either not sure about or even know to be ineffective. References 1. ten Have M, de Graaf R, van Dorsselaer S, et al. Incidence and course of suicidal ideation and suicide attempts in the general population. Can J Psychiatry. 2009;54(12):824-833. 2. Beautrais A, Fergusson D, Coggan C, et al. Effective strategies for suicide prevention in New Zealand: a review of the evidence. N Z Med J. 2007;120(1251):U2459. 3. Hacker K, Collins J, Gross- Young L, et al. Coping with youth suicide and overdose: one community's efforts to investigate, intervene, and prevent suicide contagion. Crisis. 2008;29(2):868-895. 4. Hegerl U, Althaus D, Schmidtke A, et al. The alliance against depression: 2-year evaluation of a community-based intervention to reduce suicidality. Psychol Med. 2006;36(9):1225-1233. 5. May PA, Serna P, Hurt L, et al. Outcome evaluation of a public health approach to suicide prevention in an American Indian tribal nation. …
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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 | 0.025 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.047 | 0.064 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".