Mechanisms of Stigmatization in Family-Based Prevention and Treatment of Childhood Overweight and Obesity
Bibliographic record
Abstract
It is well established that overweight and obesity are often accompanied by stigmatization. However, the influence of stigmatization on interventions for overweight and obesity remains unknown. Stigma may be particularly harmful to children. This study aimed to examine how stigmatization affects efforts to reduce childhood overweight and obesity through family interventions. This research was conducted in a socially disadvantaged area in Denmark. Twenty-seven families and forty professionals participated in in-depth interviews or workshops. The data were analyzed using CMO configurations from a realist evaluation and the theory of stigmatization developed by Link and Phelan. Thus, an abductive approach was employed in the analysis, with its foundation rooted in the empirical data. The study found that the mechanisms of stigmatization could 1. restrain professionals and parents from approaching the problem-thereby challenging family recruitment; 2. prevent parents from working with their children to avoid eating unhealthy food for fear of labeling the child as overweight or obese; and 3. cause children with obesity to experience a separation from other slimmer family members, leading at times to status loss, discrimination, and self-stigmatization. The study showed how the mechanisms of stigmatization may obstruct prevention and treatment of childhood obesity through family interventions. It is suggested that the concept of stigma should be incorporated into the program theories of interventions meant to reduce childhood overweight and obesity.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".