Attrition and the Management of Pediatric Obesity: An Integrative Review
Bibliographic record
Abstract
BACKGROUND: A key challenge in managing pediatric obesity is the high degree of program attrition, which can reduce therapeutic benefits and contribute to inefficient health services delivery. Our aim was to document and characterize predictors of, and reasons for, attrition in pediatric obesity management. METHODS: We searched literature published until January 2014 in five databases (CINAHL, EMBASE, MEDLINE, PsycINFO, and Scopus). Articles were included if they were English, included participants 0-18 years of age, focused on pediatric obesity management, incorporated lifestyle and behavioral changes without pharmacotherapy, provided attrition data, and reported information about predictors of, and/or reasons for, attrition from family-based interventions provided in research or clinical settings. Twenty-three articles (n=20 quantitative; n=2 qualitative; n=1 mixed methods) met our inclusion criteria. Clarity of study aims, objectives, methods, and data analysis were appraised using Bowling's checklist. RESULTS: Attrition varied according to definition (minimum to maximum, 4-83%; median, 37%). There were few consistent predictors of attrition between studies, although dropout was higher among US-based families receiving public health insurance. Older children were also more likely to discontinue care, but sex and baseline weight status did not predict attrition. The most commonly reported reasons for attrition were logistical barriers and programs not meeting families' needs. CONCLUSIONS: Developing and evaluating strategies designed to minimize the risk of attrition, especially among families who receive public health insurance and older boys and girls, are needed to optimize the effectiveness of pediatric obesity management.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".