IMPROVING FOOD AND NUTRITION SECURITY OF VULNERABLE COMMUNITIES - AN INTEGRATED NUTRITION SENSITIVE APPROACH
Bibliographic record
Abstract
Background: Food and Nutrition Security is a multilevel and complex construct, needing a holistic developmental approach, including multiple stakeholders. These projects were implemented by local partners, Lasoona and Doaba Foundation addressing food availability, access, use, utilization, and sustainability through a multi-sectoral approach. The aim of the evaluation was to provide a comprehensive assessment of Food and Nutrition Security projects (1086 and 1087) based on OECD DAC evaluation criteria. Methods: Mixed method approach, quasi-experimental design was used, including desk review, key informant interviews, focus group discussions with target communities, structured interviews of beneficiaries using Household Food Insecurity Assessment Scale (HIFAS) and Months of Adequate Household Food Provisioning (MAHFP) scale. Results: According to the HIFAS results, Districts of Khyber Pakhtunkhwa (KP) including Kohistan & Sawat were vulnerable with 34% and 15.78 % households facing food insecurity respectively. While in Muzaffargarh, a district of Punjab, 47% households were facing food insecurity. HDDS and IDDS improved considerably in all districts of KP and Punjab. Conclusion: The evaluated projects were social change projects sowing the seeds of a major social paradigm shift - changing the status of women at household and community level. Awareness of malnutrition and balanced diet through community volunteers, peer educators, social mobilizers and outreach workers played a pivotal role. Access and availability of diversified and nutritious food via kitchen gardens and plantation of trees with the use of organic fertilizers was encouraged.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".