Effect of maternal mental health improvement programs on obesity in pediatric populations: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Childhood obesity has become a global epidemic irrespective of the socioeconomic status of a country or nation. Obesity increases the risk of various diseases in children, for example asthma, sleep apnea, bone and joint problems, type-2 diabetes, and heart problems. The existing literature informs us of the many factors associated with childhood obesity. Among these factors, maternal mental health has been found to be a strong predictor. Maternal mental health programs were implemented to address the issue of childhood obesity but with little or no improvement. It suggests systematically reviewing the literature to assess the contents of these programs and carrying out meta-analysis for the overall effect of these interventions. METHODS: The studies included in this review will be experimental designs such as randomized controlled trials (RCTs) which provide information on interventions to improve maternal mental health and its effects on childhood obesity. We plan to search MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, PsycINFO, ERIC, CINAHL, ProQuest Dissertations and Theses Global, Scopus, and Web of Science with no restrictions as to language. Reference lists of the selected articles will also be searched for additional articles. The Cochrane EPOC Risk of Bias Tool will be used to assess the quality of studies. If the studies lend themselves to a statistical analysis, we will also carry out a meta-analysis. DISCUSSION: This review will help determine the effect of maternal health improvement programs on childhood obesity. These findings, in turn, will guide the research community on the development of related programs in the future. SYSTEMATIC REVIEW REGISTRATION: PROSPERO: CRD42017072737 .
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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 | 0.075 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.041 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.004 |
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".