Growing and Learning Together in Fostering Multisectoral Participation for Sustaining Interventions: Lessons from 3 Successive Integrated Multidisciplinary Interventions in Rural Ghana
Bibliographic record
Abstract
Despite the recognition of nutrition as a multisectoral development issue, institutional silos persist as barriers to addressing community nutrition challenges effectively and sustainably. Over the past 2 decades, 3 integrated agriculture, livelihood, nutrition, and health interventions have been implemented in rural communities across Ghana, aimed at nurturing multisectoral collaborations to enhance institutional capacity, women's empowerment, children's diets and nutritional status, and general household well-being. Using information from published articles on the interventions, workshop reports, informal institutional engagements, and field notes, insights are presented on the efforts to garner multisectoral participation to sustain these interventions. Challenges and opportunities encountered in the process of growing and learning together relative to overcoming institutional cultures, building trust, empathizing with partners' institutional challenges, making collective decisions, and building common ownership and accountability are explored. Fostering effective multisectoral participation is a dynamic process of continuous learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| 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".