A stepwise approach to designing and delivering the SCHeLTI trial community-family-mother-child obesity prevention intervention
Bibliographic record
Abstract
This paper describes the methods for the development and implementation of the Sino-Canadian Healthy Life Trajectories Initiative (SCHeLTI) intervention, part of a World Health Organization-supported effort to prevent childhood obesity through four international randomized controlled trials. SCHeLTI is a multi-center, cluster-randomized trial in Shanghai, supporting 4500 families from preconception through the child's fifth year. This Community-Family-Mother-Child intervention includes coordinated components such as Healthy Conversation sessions, nutrition consultations, breastfeeding support, an obesity clinic, and educational courses tailored to key reproductive and developmental stages and risk profiles. Guided by implementation science principles, SCHeLTI's development followed four main phases: 1) establishing the conceptual foundation (theoretical framework, outcomes, logic model); 2) building delivery infrastructure and engaging stakeholders in formative research; 3) finalizing the intervention design tailored to families' needs; and 4) implementing the intervention, including capacity building, adaptation, and process evaluation strategies.•A four-phase development process grounded in implementation science principles guided intervention design and delivery•Tailored components align with reproductive and developmental stages and risk profiles to support family and child needs across the life course•Stakeholder engagement and iterative adaptation ensured contextual relevance and feasibility.
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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 | 0.155 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".