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Record W2953365833 · doi:10.1017/s1368980019001356

Attitudes and perceptions among urban South Africans towards sugar-sweetened beverages and taxation

2019· article· en· W2953365833 on OpenAlexfundno aff
Edna Bosire, Nicholas Stacey, Gudani Mukoma, Aviva Tugendhaft, Karen Hofman, Shane A. Norris

Bibliographic record

VenuePublic Health Nutrition · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsFocus groupCynicismConsumption (sociology)Tax revenueEnvironmental healthGovernment (linguistics)PerceptionBusinessPsychologyPublic economicsPolitical scienceMedicineEconomicsMarketingPoliticsSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: A tax on sugar-sweetened beverages (SSB) was introduced in South Africa in April 2018. Our objective was to document perceptions and attitudes among urban South Africans living in Soweto on factors that contribute to their SSB intake and on South Africa's use of a tax to reduce SSB consumption. DESIGN: We conducted six focus group discussions using a semi-structured guide. SETTING: The study was conducted in Soweto, Johannesburg, South Africa, 3 months before South Africa's SSB tax was implemented. PARTICIPANTS: Adults aged 18 years or above living in Soweto (n 57). RESULTS: Participants reported frequent SSB consumption and attributed this to habit, addiction, advertising and wide accessibility of SSB. Most of the participants were not aware of the proposed SSB tax; when made aware of the tax, their responses included both beliefs that it would and would not result in reduced SSB intake. However, participants indicated cynicism with regard to the government's stated motivation in introducing the tax for health rather than revenue reasons. CONCLUSIONS: While an SSB tax is a policy tool that could be used with other strategies to reduce people's high level of SSB consumption in Soweto, our findings suggest a need to complement the SSB tax with a multipronged behaviour change strategy. This strategy could include both environmental and individual levers to reduce SSB consumption and its associated risks.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.015
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.026
GPT teacher head0.292
Teacher spread0.266 · 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

Citations48
Published2019
Admission routes1
Has abstractyes

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