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Record W4319791679 · doi:10.1111/joca.12508

Mindfulness through agency in health consumption: Empirical evidence from committed dietary supplement consumers

2023· article· en· W4319791679 on OpenAlexaff
Yun Wang, Leighann C. Neilson, Shaobo Ji

Bibliographic record

VenueJournal of Consumer Affairs · 2023
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCarleton UniversityMount Saint Vincent University
Fundersnot available
KeywordsMindfulnessAgency (philosophy)Influencer marketingEmpirical researchConsumption (sociology)Empirical evidenceSense of agencyMarketingPsychologyHealth promotionBusinessAdvertisingPublic relationsSocial psychologyPublic healthMedicineSociologyPolitical scienceClinical psychologyNursingMarketing management

Abstract

fetched live from OpenAlex

Abstract Due to individuals' increasing demand for health promotion, various health‐related consumer goods, like dietary supplements (DS), have flourished in the marketplace as magical solutions to consumers' life problems. Inevitably influenced by various marketplace stakeholders and information sources such as family, friends, brands, and social media influencers, consumers may encounter difficulties making well‐informed health decisions. Based on empirical evidence gleaned from in‐depth interviews with DS consumers, this research contributes to consumer wellness and health promotion research by confirming the role of mindfulness as an agency in distancing individuals from external social noises and enabling them to respond flexibly to inner experiences and desires. We contribute to mindful consumption research by identifying the positive impact of mindfulness on facilitating conscious and independent DS consumption decisions and health practices. Moreover, the daily practices that enable individuals to accumulate mindfulness and the impact of mindfulness on females' perception of beauty are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.175
GPT teacher head0.425
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2023
Admission routes1
Has abstractyes

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