An Integrated Approach to Harmonize Nutrition Knowledge across Sectors in the Upper Manya Krobo District of Ghana
Bibliographic record
Abstract
Determinants of nutrition are addressed by multiple service delivery platforms, including government sectors such as agriculture, education, finance, and health, in addition to community members and volunteers. Consistent messages must be integrated across these platforms to make education efforts more effective, particularly among vulnerable communities. As part of the Building Capacity for Sustainable Livelihoods and Health in Ghana ( Nutrition Links ) project, a needs assessment was conducted to identify knowledge gaps among service providers in the Upper Manya Krobo District. An integrated, multi‐level approach was subsequently developed to address these needs: (i) training workshops (e.g., Community‐Based Growth Promotion, Mother‐to‐Mother Support Group) for health sector staff and community volunteers; (ii) workshops to harmonize nutrition promotion messages (e.g., Essential Nutrition Actions) for non‐health sector (agriculture and finance institutions) staff; and (iii) Lots Quality Assurance Sampling and data analysis workshops for staff from all government sectors (health, agriculture, education, social development, environmental health, and governance) and finance institutions. These training sessions allowed participants to interact with multiple sectors and use local data to examine specific district‐wide nutrition concerns. Each session had on average 26 people in attendance and ranged from 4 to 7 days in duration. A questionnaire was administered before and after each training session and the results were compared to determine if participant knowledge had improved. All pre‐ and post‐training comparisons demonstrated an increase in participants' knowledge (p<0.0001). Training sessions with the greatest improvement included Mother‐to‐Mother Support Group for community members (mean increase = 70 ± 11 %), Essential Nutrition Actions for staff from the agriculture sector and finance institutions (mean increase = 60 ± 15 %), and Lots Quality Assurance Sampling (mean increase = 50 ± 14 %). Integrated, multi‐sector training sessions can be useful in harmonizing nutrition knowledge and may provide opportunities for improving communication and coordination of services across sectors. Support or Funding Information Global Affairs Canada, McGill University, World Vision
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".