Coping or adapting? Experiences of food and nutrition insecurity in specialised fishing households in Komodo District, eastern Indonesia
Bibliographic record
Abstract
BACKGROUND: There is growing recognition of the need for fish to be better integrated into nutrition-sensitive strategies for addressing malnutrition. Fish are overwhelmingly produced by the small-scale sector, which supports food and nutrition security directly through the provision of fish and indirectly through the generation of income which can be used to purchase other desired foods. However, there has been relatively little research on the extent of food and nutrition security in specialised fishing communities. This study assessed food and nutrition security among households in specialised fishing communities in Komodo District, eastern Indonesia. METHODS: We assessed the seasonal nutrition quality of household diets using the Food Consumption Score for nutritional analysis and food insecurity using the Household Food Insecurity Access Scale in 66 households across three communities, using a modified cluster sampling strategy. We calculated and generated descriptive statistics for these indicators with Microsoft Excel and ran a logistic generalized linear mixed model to determine factors associated with severe food insecurity using SPSS. We used semi-structured interviews and focus group discussions to understand perceptions of, change over time, and strategies for dealing with food shortfalls. RESULTS: While most households have acceptable access to nutritious foods, especially protein and heme iron-rich foods, nearly one half of households consumed vitamin A rich foods on less than 3 days of the 7-day recall period in either season. More than half of households reported experiencing a moderate or severe level of food insecurity, with higher food insecurity in the wet season. Low maternal education (OR: 3.8, 95%CI 1.5-9.9) and lower household wealth (OR: 0.5, 95%CI 0.3-0.9) were found to be associated with a severe level of food insecurity. Household's consumptive and non-consumptive response strategies reflect adaptation to chronic food insecurity but are nutritionally and economically unsustainable. CONCLUSION: Households in specialised fishing communities in Komodo District consumed diets with low diversity and experienced high levels of food insecurity. There is a need for culturally-appropriate nutrition-sensitive strategies to enhance food and nutrition security in vulnerable fishing communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".