Evaluation of complex community‐based childhood obesity prevention interventions
Bibliographic record
Abstract
BACKGROUND: Multi-setting, multi-component community-based interventions have shown promise in preventing childhood obesity; however, evaluation of these complex interventions remains a challenge. OBJECTIVE: The objective of the study is to systematically review published methodological approaches to outcome evaluation for multi-setting community-based childhood obesity prevention interventions and synthesize a set of pragmatic recommendations. METHODS: MEDLINE, CINAHL and PsycINFO were searched from inception to 6 July 2017. Papers were included if the intervention targeted children ≤18 years, engaged at least two community sectors and described their outcome evaluation methodology. A single reviewer conducted title and abstract scans, full article review and data abstraction. Directed content analysis was performed by three reviewers to identify prevailing themes. RESULTS: Thirty-three studies were included, and of these, 26 employed a quasi-experimental design; the remaining were randomized control trials. Body mass index was the most commonly measured outcome, followed by health behaviour change and psychosocial outcomes. Six themes emerged, highlighting advantages and disadvantages of active vs. passive consent, quasi-experimental vs. randomized control trials, longitudinal vs. repeat cross-sectional designs and the roles of process evaluation and methodological flexibility in evaluating complex interventions. CONCLUSIONS: Selection of study designs and outcome measures compatible with community infrastructure, accompanied by process evaluation, may facilitate successful outcome evaluation.
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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 | 0.061 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".