Evaluation of community-based health promotion interventions in children and adolescents in high-income countries: a scoping review on strategies and methods used
Bibliographic record
Abstract
BACKGROUND: In recent decades, community-based interventions have been increasingly adopted in the field of health promotion and prevention. While their evaluation is relevant for health researchers, stakeholders and practitioners, conducting these evaluations is also challenging and there are no existing standards yet. The objective of this review is to scope peer-reviewed scientific publications on evaluation approaches used for community-based health promotion interventions. A special focus lies on children and adolescents' prevention. METHODS: A scoping review of the scientific literature was conducted by searching three bibliographic databases (Medline, EMBASE, PsycINFO). The search strategy encompassed search terms based on the PCC (Population, Concept, Context) scheme. Out of 6,402 identified hits, 44 articles were included in this review. RESULTS: Out of the 44 articles eligible for this scoping review, the majority reported on studies conducted in the USA (n = 28), the UK (n = 6), Canada (n = 4) and Australia (n = 2). One study each was reported from Belgium, Denmark, Germany and Scotland, respectively. The included studies described interventions that mostly focused on obesity prevention, healthy nutrition promotion or well-being of children and adolescents. Nineteen articles included more than one evaluation design (e.g., process or outcome evaluation). Therefore, in total we identified 65 study designs within the scope of this review. Outcome evaluations often included randomized controlled trials (RCTs; 34.2%) or specific forms of RCTs (cluster RCTs; 9.8%) or quasi-experimental designs (26.8%). Process evaluation was mainly used in cohort (54.2%) and cross-sectional studies (33.3%). Only few articles used established evaluation frameworks or research concepts as a basis for the evaluation. CONCLUSION: Few studies presented comprehensive evaluation study protocols or approaches with different study designs in one paper. Therefore, holistic evaluation approaches were difficult to retrieve from the classical publication formats. However, these publications would be helpful to further guide public health evaluators, contribute to methodological discussions and to inform stakeholders in research and practice to make decisions based on evaluation results.
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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 | 0.143 | 0.251 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.032 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".