The Multiple Dimensions of Participation: Key Determinants of Nutrition Intervention Outcomes
Bibliographic record
Abstract
Nutrition research benefits from broad and intensive participation by stakeholders. The articles in this series demonstrate that understanding participation is complex because it incorporates the dimensions of stakeholders, activity, time, and intensity. Early involvement in research can help prioritize the problems to be addressed, refine the specific research question, and determine acceptable community-based approaches to be used in an intervention. The included studies examined the construct of participation and the diverse means by which it can be measured. They demonstrated how knowledge gained from early participation influenced the direction of interventions and increased relevancy for the community. The researchers assessed participation intensity during the intervention phase to help explain project outcomes and provide estimates of the magnitude of the effect that could be achieved if high-level participation of stakeholders was universal. In addition, participation in the analysis process was a key component of some of the articles in this series, demonstrating the richness of understanding that can be obtained through collaborative analyses. The included papers provide insight into how to define and measure participation, how to explore approaches to encourage participation of direct and indirect beneficiaries, and how participation at different time points and by different stakeholders can validate and support interventions and enhance effectiveness. As such, the series serves as a valuable reference to researchers, program and policy designers, implementers, and evaluators to increase the benefits of community-based interventions for nutrition outcomes.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".