Suicide Prevention Strategies: Adventures in the Grey Zone.
Notice bibliographique
Résumé
Knowledge about psychopathology in young persons, its high prevalence and potential negative consequences, is reaching wider audiences. Statistics show that the number of Canadian children and adolescents experiencing severe and impairing mental health conditions exceeds one and a half million. Deaths by suicide, the leading cause of non-accidental death among youth in Canada, outnumber all deaths by diseases of the heart, lungs, kidney, gastrointestinal system and cancer combined. Unquestionably, this is a serious threat to public health and to the human capital of our nation; however, effective strategies to address this menace have been elusive. The quest for efficacious initiatives is not only compelling, but is expected in a society that values the young and invests in its future. We need programs that are creative and appealing, yet, more importantly, that provide clear benefit and absence of harm. In our feature article, Wei, Kutcher and LeBlanc (2015) offer a critical appraisal of two suicide prevention programs: Signs of Suicide and Yellow Ribbon, These programs are extensively used in North America. The SOS Signs of Suicide program has been implemented in more than 7,000 schools in the United States, Canada, and Ireland. SOS was developed with sponsorship from the U.S. Department of Health and Human Services Substance Abuse and Mental Health Services Administration (SAMHSA). Yellow Ribbon emerged as a grassroots initiative and follows the Canadian National Suicide Prevention Strategy. Yellow Ribbon encourages teens to seek help in times of crisis and trains “trusted adults” to recognize the symptoms, risk factors, and heightened signs of suicide. The core strategy of the Yellow Ribbon is for adolescents at risk to learn and act of the mantra “It’s OK to Ask 4 help.” The review by Wei, Kutcher and LeBlanc of these two suicide prevention initiatives is rather sobering, as it concludes that, by and large, evidence emerging from comparative studies of the two prevention initiatives is either insufficient or inconclusive. Even more discouraging is the fact that, when brought to test, one of the programs is ranked as ineffective. In spite of increased knowledge regarding depression and suicide among program participants, the effect on help-seeking is rather marginal or absent – very disappointing indeed. Wei and colleagues also raise critical methodological concerns pertaining to the choice of primary outcome measures in the noted studies; a salient one being that not a single study elected to measure differences in suicide death. Although, not entirely in disagreement with Wei, Kutcher and LeBlanc, it is pertinent to raise the question as to whether we are facing an essential epistemological problem; a problem not just restricted to the appraisal of suicide prevention programs, but also influencing the interpretation of knowledge pertaining to the evaluation of health and mental health intervention studies. A recent commentary that appeared in the New England Journal of Medicine (Chandra, 2015) speaks about the preference given to the interpretation of interventions through a binary lens: all white or all black, and no shades of grey. Within this framework, interventions are either appropriate or inappropriate, either effective or ineffective. As a consequence, the presence of grey zones is negated. However, a great deal of prevention, promotion, and treatment in health and mental health care takes place in a grey zone where benefits are less than certain. Arguably, in some circumstances, even if only a few would benefit, implementation of the intervention might be appropriate and defensible; particularly valid for conditions in which a multitude of factors contribute to risk as well as resilience. A final word of caution: interpretation of intervention effectiveness through a binary lens where shades of grey are rejected might be associated with adverse effects. Rejection of the suicide prevention initiatives under scrutiny would only be justified when and if something better can be offered.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,011 |
| Communication savante | 0,007 | 0,016 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,010 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».