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Enregistrement W883540627

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

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

Notice bibliographique

RevueOtago University Research Archive (University of Otago) · 2015
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueIncome, Poverty, and Inequality
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLow incomeNutrientFood intakeAgricultural economicsSupplemental Nutrition Assistance ProgramEconomicsBusinessEnvironmental healthDemographic economicsFood insecurityGeographyFood securityMedicineBiologyAgriculture
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,336
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,060
Tête enseignante GPT0,318
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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