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Record W2603693392

Nutrition Education and the Cost of Healthy Food - Do They Collide? Lessons Learned in a Predominantly Black Urban Township in South Africa

2013· article· en· W2603693392 on OpenAlexaboutno aff
Moïse Muzigaba, Thandi Puoane

Bibliographic record

VenueInternational public health journal · 2013
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingSocioeconomic statusConsumption (sociology)Work (physics)Position (finance)PerceptionBusinessMarketingPsychologyPolitical sciencePopulationSociologyEnvironmental healthMedicineEngineeringSocial science
DOInot available

Abstract

fetched live from OpenAlex

IntroductionIncreasing evidence in some developed countries highlights the importance of examining the influence of individual-level (compositional) socio-economic and environmental (contextual) factors on healthy food-purchasing and consumption behaviour. Various studies around this subject have emerged mainly in some European countries (1-3), the USA (4, 5), Canada (6) and Australia (7, 8). Although there have been contradictory findings, much of this work consistently corroborates the view that the locally available retail outlets in addition to the socio- economic status of individuals influence food- purchasing behaviour. This suggests that in addition to individual responsibilities towards their health, the contextual forces which shape decisions people make and the behaviours in which they engage should not be underestimated if the complex world of food- purchasing behaviour is to be clearly understood. Figure 1 which we remodeled based on previous work by White (9) and Story et al (10) illustrates a theoretical framework of inter-relationships of compositional and contextual factors which may act singly or collectively to influence food-purchasing behaviour at the household level.The broad logic of this theoretical framework is that socioeconomic position- which may itself be determined by such factors as the family background, inherited wealth, educational achievement and employment status amongst other things - may determine the perceptions individuals have towards their ability to access and/or afford healthy foods. Similarly, the socioeconomic position may dictate individual's ownership of material resources (e.g. own transport, certain cooking utensils, a fridge, etc.) necessary to easily access, prepare, and/or store healthier foods. This model also argues that the less educated people are, the less likely they will be able to make healthy food purchasing decisions.However, the framework also acknowledges the role that environmental parameters play in shaping food purchasing decisions that individuals make based on their socioeconomic positions. For example, the differences in cost and availability between healthier foods and their less healthy options, and the relative ease of access to retail outlets which sell these foods may all determine the healthiness of the food people buy.In South Africa, there is still a dearth of evidence concerning the cost and availability of healthy food. Although some researchers (11-16) have begun to explore this area, there is still a need for evidence that substantiates and complements their findings. Public health efforts geared towards promoting healthy food consumption behaviour - such as the development and implementation of the South African Food-base Dietary Guidelines (SAFBDGs) may have little relevance in settings where there is poor access to affordable healthy foods and as such, nutrition education is likely to have little effect.To substantiate this supposition, we conducted the current study based on an earlier interventional study in which a community-based model was developed to address lifestyle factors that contributed to the burden of non-communicable diseases in Khayelitsha (17), a predominantly black urban township in Cape Town, South Africa. During this intervention, community health workers (CHWs) first received training on the prevention of risk factors for non-communicable diseases focusing on diet and physical activity and on how to run a community- based health club. They then recruited individuals from the surrounding communities to join a health club in which nutritional education based on the SAFBDGs and physical activity sessions were conducted on a regular basis (17).Despite these efforts however, some of the health club members (HCMs) still made unhealthy food choices. We then sought to investigate and describe HCM's experiences in buying healthier food items and to compare respondents' perceived cost of selected healthier foods and their less healthy counterparts with actual market costs in a South African township. …

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.284
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.074
GPT teacher head0.354
Teacher spread0.280 · 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.

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".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

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