Access to Information Technologies and Consumption of Fruits and Vegetables in South Africa: Evidence from Nationally Representative Data
Bibliographic record
Abstract
Extensive evidence indicates that fruit and vegetable (F+V) consumption leads to reduced chances of diet related non-communicable diseases (NCDs). However, the F+V consumption levels remain low. This paper investigates the extent to which access to information technologies improves F+V consumption in South Africa. A nationally representative sample of 20,908 households was analysed using the Poisson and logit regression models. The study results indicated that most households do not consume sufficient F+V per day. Only 26% of the household heads consumed F+V at least five times a day. Access to mobile phones, radio, television, and internet was associated with increasing frequency of F+V consumption, and higher chances that a household would consume the minimum recommended levels. The association between the communication technologies and F+V consumption varied. Television access had the highest association with both foods, while internet was only significantly associated with vegetable consumption. Several demographic and socio-economic factors played a key role in shaping F+V consumption patterns. The results show that there is scope to disseminate nutrition awareness and education programs, through mobile phones, internet, radio and television in South Africa. The interventions to promote F+V consumption should be tailored according to the different socio-economic profiles of the population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".