Psychopathology in severely obese women from a Canadian bariatric setting
Bibliographic record
Abstract
Purpose – Evidence suggests high rates of psychiatric disorders in bariatric surgery candidates (e.g. Mitchell et al., 2012), although no rigorous studies have examined the prevalence in a Canadian sample. Improved understanding of the prevalence of psychopathology among female patients is an important area of study, as females comprise approximately 80 percent of surgical candidates (Martin et al., 2010; Padwal, 2005). The purpose of this paper is to assess the prevalence of Axis I disorders and associations with quality of life in a Canadian sample of female bariatric surgery candidates. Design/methodology/approach – Female patients (n=257) were assessed using a structured psychodiagnostic interview and completed a health-related quality of life questionnaire. Findings – Results indicated that 57.2 percent of patients met DSM-IV-TR criteria for a lifetime psychiatric disorder and 18.3 percent met criteria for a current psychiatric disorder. Major depressive disorder was the most common lifetime psychiatric disorder (35.0 percent) and binge eating disorder was the most prevalent current psychiatric disorder (6.6 percent). Patients scored significantly lower than Canadian population norms on all domains of the SF-36 (all p's<0.001). Patients with a current Axis I disorder also reported significantly worse functioning on four mental health domains and one physical health domain (p's<0.01) compared to patients without a current Axis I disorder. Originality/value – Results confirm high rates of psychiatric disorders in Canadian female bariatric surgery candidates and provide evidence for associated functional health impairment. Further study is needed to elucidate how pre-operative psychopathology may impact female patients’ post-operative outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".