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Record W883540627

Variation in nutrient and food intake over pay cycles among low income households: A Pilot Study

2015· dissertation· en· W883540627 on OpenAlexaboutno aff
Emma Jayne Phillips

Bibliographic record

VenueOtago University Research Archive (University of Otago) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsLow incomeNutrientFood intakeAgricultural economicsSupplemental Nutrition Assistance ProgramEconomicsBusinessEnvironmental healthDemographic economicsFood insecurityGeographyFood securityMedicineBiologyAgriculture
DOInot available

Abstract

fetched live from OpenAlex

Background
\nEconomic factors are one of the greatest risks to household food insecurity. In New Zealand, the proportion of low income households is steadily increasing, contributing to the rising income inequality gap. Research among low income households in Canada has shown that food and nutrient intake declines over a pay cycle. Such studies have not been replicated in New Zealand. Many studies have demonstrated that mothers will sacrifice both their own intake quality and quantity, in order to protect their child’s diet. The main objective of this study was to answer whether nutrient and food intake declines over the pay cycle of a typical low income New Zealand households, and whether this differs between caregivers and children.
\nMethods and procedures
\nThis was an observational pilot study based in Dunedin. Data were collected from 15 low income (<$45,000 per year) households with children (5-12years). The main food preparers from each household were interviewed. Information was collected on demographics, participant feedback, and household food insecurity status. The primary food preparer also completed two diet records. One was for them and the other was on behalf of one randomly selected child within the household. These took place at four time points during the household pay cycle, which was either weekly or fortnightly. T1 refers to the time point closest to receiving their main source of income, through to T4, which was allocated at the end of the cycle. In addition to this, food items were coded via their sources (e.g. supermarket, charitable aid and family or friend) and a record of all household food expenditure was collected. Both nutrient and food group intakes were determined using the dietary assessment software Kai-culator. The mean nutrient intakes between women and children were then compared and trends in food and nutrient intakes over the pay period were identified visually.
\nResults
\nAll diet record days were completed and all questions were answered in the interviews. On collection of all data, food eaten outside the home was the only area which noted participant difficulty. A limitation of the current questionnaires was the observed discrepancies in the reported main source of income amount. Sixty percent of the households were categoried as moderately food insecure and twenty percent experienced low food security. Women reported various strategies and sacrifices made in response to declining resources. They also had a lower intake of energy in an overall comparison to children. Additionally, the children’s intake of fruit and dairy products was double that of their mothers. In women, a decline in fruit and calcium intake was observed across T1 to T4, however, this was not replicated in the children.
\nSummary/conclusion
\nDespite its small sample size, the results suggest that a decline in nutrient and food intake over the pay cycle was present in women. This trend was not found in children, thus suggesting differences between caregivers and children. A potential reason for this difference could be maternal attempts to protect children from the harshest effects of food insecurity. Such a proposition is supported by the poorer intake quality and lower nutrient intake observed in women. The results of this study justify carrying out a larger study. Issues surrounding the collection of income data need to be addressed and a more in-depth qualitative analysis included.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.318
Teacher spread0.258 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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