Notice bibliographique
Résumé
In 2007, following a comprehensive report by a Senate Committee, the Government of Canada announced funding for the launch of a mental health commission.1 This move was timely and sought to address the considerable burden of mental illness in Canada. A parallel epidemic of obesity, currently affecting 1 in 4 adult Canadians and as many as 1 in 10 Canadian children,2 has yet to receive similar attention. Interestingly enough, however, these 2 major health problems affecting millions of Canadians may be much closer related to each other than is evident at first glance. While obesity is largely regarded as a simple consequence of lifestyle choices, with both public health and individual interventions focusing primarily on measures to promote healthy eating and physical activity, its close link to mental health, as one of its key determinants, is often missed. In this edition of The Canadian Journal of Psychiatry, Dr Valerie H Taylor and colleagues34 present 2 articles discussing the complicated and complex links between mental health and excess weight, and provide a brief summary of current approaches to obesity management. As pointed out in the first article,3 there is considerable overlap between the mental health and obesity co-epidemics. Not only do the vast majority of treatment-seeking obese people present with a wide range of mental health issues, mental illness, in turn, can often promote weight gain and prove a major barrier to obesity management. As Dr Taylor and colleagues3 discuss, the latter is not simply a matter of obesogenic psychiatric medications promoting weight gain - the links between obesity and mood disorders, anxiety disorders, attention disorders, addiction disorders, psychotic disorders, personality disorders, and trauma (to name a few) invoke societal, cognitive, behavioural, and biological factors that interact in complex and complicated ways. Thus I have previously proposed that even a cursory assessment of mental health should be an integral part of every assessment for obesity.5 In addition, mental illness must be considered as a possible etiological factor in anyone presenting with weight gain attributable to overeating and undermoving.6 Not surprisingly, as outlined in the second article,4 current treatments for obesity borrow freely from the behavioural and pharmacological arsenal of mental health interventions. Cognitive-behavioural therapy, interpersonal therapy, motivational interviewing, and other techniques, well established in the treatment of mental health and addictions, are increasingly recognized for their role in obesity management. In addition, current pipelines for the pharmacological treatment of obesity include drugs targeting the serotonergic, dopaminergic, endocannabinoid, opiate, and other systems within the peripheral and central nervous systems. Given the importance of the central nervous system as a prime determinant of ingestive and activity behaviour, it is also not surprising that current obesity research uses a wide range of psychological assessments and neuroimaging techniques to better define the obesity phenotype. …
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».