Weight-control compensatory behaviors patterns and correlates: a scoping review
Bibliographic record
Abstract
Objective: Weight-control compensatory behaviors appear to be a commonly utilized strategy for health management. Individuals engaging in such behaviors believe that the negative consequences from unhealthy behaviors will be neutralized by the positive consequences of healthy behaviors. Existing research has not reached a consensus on whether such behaviors are beneficial to health. This review aims to (1) summarize the patterns of weight-control compensatory health behaviors in different populations, (2) highlight correlates, predictors, and consequences of compensatory health behaviors, and (3) identify gaps for future research. Method: This review identified existing literature using online databases, CINAHL and PubMed. Primary research articles published after 2000 with non-clinical participants of 12 years or older who engaged in compensatory behaviors for weight control purposes were selected. Descriptive statistics were extracted from 35 studies. Results: Different patterns for weight-control compensatory behaviors emerged between the female and male sexes. Meanwhile, no clear association of such behaviors was found across weight status. Studies reviewed also highlighted three main areas of compensatory behaviors for weight management, namely dietary behaviors, physical activity, and alcohol consumption. Weight-control compensatory behaviors had significant negative correlations with mental health indicators, such as psychosocial functioning, emotional differentiation ability, and body esteem. Conclusion: Weight-control compensatory behaviors may be a widely used weight management strategy and can be presented in diverse ways. Although believed to be promoting health, such behaviors appear to be associated with poor psychological well-being. This emerging topic warrants more in-depth investigation to establish the direction of causation. Future research may investigate the relationship between weight-control compensatory behaviors and various aspects of health over longer time periods, examine the engagement of multiple weight-control compensatory behaviors, and focus on high-risk populations.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".