The Association between Attachment Avoidance and Quality of Life in Bariatric Surgery Candidates
Bibliographic record
Abstract
OBJECTIVE: Patients presenting for bariatric surgery have high rates of psychiatric co-morbidity and reduced health-related quality of life (HRQOL) compared to the general population. In this study, we aimed to determine the relationship between insecure attachment styles and HRQOL in bariatric surgery candidates. METHODS: We assessed depression, social support, attachment avoidance, attachment anxiety, HRQOL (SF-36), and eating disorder psychopathology in 70 consecutive patients assessed for bariatric surgery. SF-36 physical (PCS) and mental component scores (MCS) were compared to a normative sample and analyzed using t-tests. Predictors of HRQOL were analyzed using multiple linear regression analyses. RESULTS: SF-36 PCS and MCS in this pre-bariatric surgery sample were significantly lower than in an age-matched reference population. Depression, attachment anxiety, attachment avoidance and eating disorder psychopathology scores were negatively correlated with SF-36 MCS. Depression was associated with lower SF-36 PCS (p = 0.015). SF-36 MCS were significantly predicted by BDI scores (p < 0.001) and attachment avoidance (p = 0.024) in our multiple regression model. CONCLUSION: This is the first study to demonstrate an association between attachment avoidance and poor mental HRQOL in bariatric surgery candidates. Future studies are needed to examine the effect of attachment avoidance on post-bariatric surgery 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".