The Utilization of Process Evaluations in Childhood Obesity Intervention Research: A Review of Reviews
Bibliographic record
Abstract
Process evaluations are an essential component to evaluating health promotion programs, however they are consistently under-utilized and oftentimes not reported upon in the literature. This study reports the use of process evaluations in childhood obesity prevention interventions implemented over the past three decades. Seven meta-analyses and systematic reviews were located for this review or reviews, and from these, 119 unique references were identified. Each article was retrieved and read for appropriateness, and 20 were excluded for a variety of reasons (ex. not published in English language), resulting in 99 articles included for this study. Overall, process evaluations were not well reported upon. Only 38 studies reported the fidelity of program implementation, 25 studies tracked participant attendance, 29 studied evaluated participant satisfaction, and 49 studies reported how staff members were trained. Additionally, one-third of the studies did not report using a single type of process evaluation, and only 5 studies reported using all four types. Results from this study suggest that the use of process evaluations has been low in this area of research, which may explain why many obesity prevention studies have reported mixed or modest results. Suggestions for implementing simple, yet effective process evaluations in future studies will be presented.
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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 | 0.068 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".