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Record W4226109814 · doi:10.1080/21642850.2022.2053686

Examining dietary self-talk content and context for discretionary snacking behaviour: a qualitative interview study

2022· article· en· W4226109814 on OpenAlexaff
J. Rose, Rebecca Pedrazzi, Stephan U Dombrowski

Bibliographic record

VenueHealth Psychology and Behavioral Medicine · 2022
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSnackingPsychologyAutomaticityContext (archaeology)Qualitative researchFood choiceContent analysisSocial psychologyCognitionDevelopmental psychologyObesityMedicine

Abstract

fetched live from OpenAlex

Background: Consuming discretionary snack foods high in calories, salt, sugar or fat in between regular meals can have a negative impact on weight management and health. Despite the intention to refrain from discretionary snacking, individuals often report feeling tempted by snack foods. A cognitive process to resolve food choice related tension may be dietary self-talk which is one’s inner speech around dietary choice. This study aimed to understand the content and context of dietary self-talk before consuming discretionary snack foods.Methods: Qualitative semi-structured interviews based on Think-Aloud methods were conducted remotely. Participants answered open-ended questions and were presented with a list of 37 dietary self-talk items. Interview transcripts were analyzed thematically.Results: Interviews (n = 18, age: 19–54 years, 9 men, 9 women) confirmed the frequent use of dietary self-talk with all 37 content items endorsed. Reported use was highest for the self-talk items: ‘It is a special occasion’; ‘I did physical activity/exercise today’; and ‘I am hungry’. Three new items were developed, eight items were refined. Identified key contextual themes were: ‘reward’, ‘social’, ‘convenience’, ‘automaticity’, and ‘hunger’.Conclusions: This study lists 40 reasons people use to allow themselves to consume discretionary snack foods and identifies contextual factors of dietary-self talk. All participants reported using dietary self-talk, with variation in content, frequency and degree of automaticity. Recognising and changing dietary self-talk may be a promising intervention target for changing discretionary snacking behaviour.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.549
GPT teacher head0.575
Teacher spread0.025 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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