Barriers to and enablers of modifying diet after metabolic bariatric surgery: A systematic review of published literature
Bibliographic record
Abstract
This is a qualitative systematic review in which we investigated barriers and enablers influencing dietary behavior change after metabolic bariatric surgery (MBS). Database searches retrieved publications reporting perceived factors influencing dietary behavior change post-MBS. Data (quotes, survey results, interpretative summaries) were extracted and analyzed using combined deductive and inductive thematic analyses. The generated barrier/enabler themes mapped to the Theoretical Domains Framework and then behavior change techniques to identify potential strategies to improve post-operative dietary behavior. Thirty-four publications were included. Key barriers fell within the domains of 'Environmental Context and Resources' (e.g., insufficient and unreliable healthcare services), 'Behavioral Regulation' (e.g., lack of self-discipline), 'Emotions' (e.g., eating as a strategy to overcome negative emotions), 'Beliefs about Consequences' (e.g., the extent of realistic expectations from MBS), and 'Social Influences' (e.g., challenge of eating at social events). Key enablers were also identified within 'Environmental Context and Resources' (e.g. self-access internet-based resources), 'Behavioral Regulation' (e.g. learning how to develop new dietary strategies), 'Beliefs about Consequences' (e.g., positive impacts of surgery-induced food intolerances), and 'Social Influences' (e.g., support from social/group sessions). Potential strategies to change postoperative dietary behavior include social support, problem-solving, goal setting, and self-monitoring of behavior. This provides insight into the targets for future post-operative nutrition-focused interventions.
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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 | 0.028 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".