Strategies to reduce attrition in managing paediatric obesity: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of the literature for strategies designed to reduce attrition in managing paediatric obesity. METHODS: We searched Ovid Medline (1946 to May 6, 2020), Ovid Embase (1974 to May 6, 2020), EBSCO CINAHL (inception to May 6, 2020), Elsevier Scopus (inception to April 14, 2020), and ProQuest Dissertations & Theses (inception to April 14, 2020). Reports were eligible if they included any obesity management intervention, included 2 to 18 year olds with overweight or obesity (or if the mean age of participants fell within this age range), were in English, included experimental study designs, and had attrition reduction as a main outcome. Two team members screened studies, abstracted data, and appraised study quality. RESULTS: Our search yielded 5,415 original reports; six met inclusion criteria. In three studies, orientation sessions (n = 2) and motivational interviewing (MI) (n = 1) were used as attrition-reduction strategies before treatment enrollment; in three others, text messaging (n = 2) and MI (n = 1) supplemented existing obesity management interventions. Attrition-reduction strategies led to decreased attrition in two studies, increased in one, and no difference in three. For the two strategies that reduced attrition, (a) pre-treatment orientation and (b) text messaging between children and intervention providers were beneficial. The quality of the six included studies varied (good [n = 4]; poor [n = 2]). CONCLUSION: Some evidence suggests that attrition can be reduced. The heterogeneity of approaches applied and small number of studies included highlight the need for well-designed, experimental research to test the efficacy and effectiveness of strategies to reduce attrition in managing paediatric 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 | 0.044 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".