Trends in the Contribution of Perceived, Received, and Integrative Social Support to Food Security
Bibliographic record
Abstract
Social support (SocSp) is the perception and/or actuality of having assistance available from others and belonging to a supportive network. This study examines the potential contribution of perceived support (belief that one can count on others), received support (receipt of material goods), and integrative support (belonging to a community) to one's food security status. The Gallup World Poll (GWP), a nationally representative survey of adult individuals in 150 countries, incudes the Food Insecurity Experience Scale (FIES). Four questions related to SocSp were selected from the GWP questionnaire and analyzed to determine their relationship to respondents' food security. Respondents who answered “don't know” or “refused” to any of the eight items of the FIES were excluded from the analysis. Using data from 42 countries (approximate n=1000/country), 40.8% of the interviewees were categorized as food secure and 59.2% food insecure (which includes mildly, moderately and severely food insecure). Logistic regression models were developed and adjusted for income, country and area of residence, gender, education, age, and number of children in the household. The odds of being food insecure were significantly higher for those who did not perceive they had someone to count on locally (OR = 2.11, 95% CI: 1.96, 2.27) or abroad (OR = 1.31, 95% CI: 1.23, 1.39) and for those dissatisfied with their ability to meet people (OR = 1.56, 95% CI: 1.43, 1.69). This research provides evidence of the contribution of perceived and integrative SocSp to food security. Moreover, it confirms the importance of distinguishing between types of SocSp for in‐depth understanding of the experience of food insecurity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".