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Record W4392019192 · doi:10.1093/heapro/daae006

Qualitative analysis of UK women’s attitudes to calorie-based alcohol marketing and alcohol calorie labelling

2024· article· en· W4392019192 on OpenAlexaff
Amanda Atkinson, Beth Meadows, Erin Hobin, Lana Vanderlee, Harry Sumnall

Bibliographic record

VenueHealth Promotion International · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité LavalPublic Health Ontario
FundersEconomic and Social Research Council
KeywordsLabellingAlcoholCalorieQualitative researchEnvironmental healthQualitative analysisPsychologyMedicineAdvertisingBusinessSociologyCriminologyChemistry

Abstract

fetched live from OpenAlex

Mandatory standardized nutritional information on alcoholic drinks such as energy, or calorie labelling, is a population-level public health measure aimed at addressing obesity and alcohol consumption. In the UK, such measures are not a statutory requirement, but some alcohol brands do include references to calories on their products and in their marketing materials, as a marketing strategy to encourage sales and consumption. This article presents findings of semi-structured individual (N = 43) and group (N = 9) interviews with 78 women living in the UK that aimed to gain insight into their attitudes towards calorie-based alcohol brand marketing, and alcohol calorie labelling (ACL) as a health policy. Three themes are presented that outline how women rejected calorie marketing and labelling; the potential positive and unintended impact on alcohol consumption and dietary/eating practices; and how views on calorie labelling were intertwined with women's attitudes towards marketing that draws on calorie messaging. A feminist anti-diet discourse, as well as a discourse of pleasure through alcohol consumption, was at play in women's accounts, which may limit the intended aims of ACLs. It is concluded that ACLs should be considered within the wider commercial context of alcohol marketing that draws on calories to promote sales and consumption, consideration of the gendered factors that may lead some to reject ACLs as a health policy, and the potential for unintended consequences.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.494
Teacher spread0.389 · 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.

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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

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