Impact of behaviour change communication interventions on sales of fortified sunflower oil in Tanzania: A spatial–temporal analysis and association study
Bibliographic record
Abstract
The Masava project was implemented in Manyara and Shinyanga regions in Tanzania to improve vitamin A intake by making available vitamin A-fortified sunflower oil with a subsidy through a mobile phone-based e-Voucher system. This study was conducted to assess the impact of the behaviour change communication (BCC) campaign of the project on volume of sales of vitamin A-fortified sunflower oil. The e-Voucher system provides real-time data on the number of e-Vouchers redeemed. The number, type, and locations of BCC events were obtained from the implementation agency. Multivariate linear regression was used to examine the associations between (a) the number and type of BCC events conducted in a ward and the volume of subsequent fortified oil redeemed in the ward and (b) distance of clinic shows, a component of the BCC campaign, from participating retailers and the volume of fortified oil redeemed in the store. After 1 year of the campaign, the volume of fortified oil redeemed monthly increased by more than 5 times in Manyara and by more than three times in Shinyanga. Among the different types of BCC events conducted, only clinic shows and cooking shows were significantly associated with the volume of redemptions (p < .05). Compared with retailers where at least one clinic show was conducted within 0.5 km from its location, the volume of redemptions was significantly lower at retailers where no clinic show conducted within 3.0 km from its location (p < .05). These findings suggest that future health promotion interventions in rural Africa should involve health clinics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".