Bibliographic record
Abstract
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. …
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".