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Food Insecurity and it’s Predictors Among Vulnerable Children

2013· article· en· W3148481885 on OpenAlexvenueno aff
Abok Ibrahim Ishaya, Yilgwan Christopher Sabo, Collins John

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2013
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFood insecurityEnvironmental healthFood securityAgriculture

Abstract

fetched live from OpenAlex

Background: To determine the prevalence of food insecurity and some socio demographic predictors of food insecurity among Vulnerable Children (VC) in Jos, North- central Nigeria. Methods: A cross-sectional comparative study involving 202 VC selected using multi-stage sampling technique across two orphanages and three communities located in sub-urban areas in Jos East, Jos North and Jos South Local Government Area was carried out. A VC was defined as a child who has loss mother, father or both or children who reside with chronically ill parents or reside in institution during the study. Only VC greater than five years but less than 18 years were enrolled. Food security was measured using four questions that were adapted from existing questionnaires. Food insecurity was defined and graded has mild, moderate or high if there was an affirmative response to any one, two or three of four questions. Data generated were analyzed using EPI Info version 3.65 software. The independent variables orphan status, age, gender, place of residence, child level of education, child work, were compared with the dependent variables of food insecurity using bivariate and multivariate analysis. In all statistical test p < 0.05 was considered statistically significant. Results: Of the 202 VC analyzed 38.6 %(78) were girls and 61.4 %(124) were boys with a mean age of 12.7+ 2.6 years. One hundred and two (50.5%) were IVC while 100(49.5%) were HVC. The VC were mostly orphans (83.2% [168]) while 16.8 %(34) were non orphans. All children were enrolled into school, 137 were in primary school, while the rest were in secondary school. Majority of the HVC were cared for by their mother (24.8% 50[VC]), father (1.9% [4]), uncles (8.4% [17]), aunts (10% [5.0]), grandparents (5.4% [11]), and non relatives (8% [4.0]). The overall prevalence of food insecurity was 48.5%. Of the 98 Food insecure VC 65% were HVC compared to 35% observed among IVC(p <0.05); 69.6 % were children older than 12 years compared to 30.4% obsereved in VC who were <12 years. The odds of food insecurity was 2.1 times in older VC aged 13-18 years (CI=1.1-3.9). VC attending Secondary School were 1.9 time likely to be food insecure compared to those in primary school (CI=1.1-3.5). Similarly, HVC were 3.6 times more likely to be food insecure compared to IVC. (CI=1.9-6.9). VC who worked to earn money had a 2.8times odd to be food insecure (CI=1.2-6.24). Paternal orphans were 2.4 times more likely to be food insecure (CI= 1.0-6.5) compared to other group of VC. Being a maternal orphan, a double orphan or non orphan VC does not predict food insecurity. Sexual experience was also not a predictor of food insecurity. Conclusion: The implication of hunger, in an adolescent child who considered himself/herself overworked is enormous on child physical, emotional and social development. This might lead to more children living their homes to seek shelter in orphanages were the food security status even though not perfect is better than the household. This can be prevented if Household VC are actively identified and their families supported with programs that can make them food secure.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.045
GPT teacher head0.378
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2013
Admission routes1
Has abstractyes

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