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Record W2883098763 · doi:10.32920/cd.v4i1.717

Thresholds of size: An interpretative phenomenological analysis of childhood messages around food, body, health and weight.

2018· article· en· W2883098763 on OpenAlexvenueno aff
Fiona Holland, Karin E. Peterson, Stephanie Archer

Bibliographic record

VenueJournal of Critical Dietetics · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsDietingInterpretative phenomenological analysisOverweightPsychologyDevelopmental psychologyQualitative researchSocial psychologyObesityWeight lossMedicineSociology

Abstract

fetched live from OpenAlex

This study explores the lived experiences of non-dieting, middle-aged Western women classified as ‘overweight’ or ‘obese’ on BMI charts. Qualitative research that has focused on non-weight loss experiences with this population has been rare. Four women from aged 40-55 were interviewed about their early messages and experiences around food, body, health and weight. An interpretative phenomenological analysis was conducted. Three themes were identified: 1) family culture and body norms 2) thresholds of size and 3) action and outcome. Participants identified a range of influences upon their early body appraisal, with parents, extended family, peers and community members contributing to their understanding of what constituted as an acceptable size. The impact upon their sense of identity and emotional wellbeing is discussed. This study contributes to the role of the modelling and messages around size and value given by important others and the psychological ramifications these can have over time.

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0070.015
Scholarly communication0.0050.005
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.478
Teacher spread0.412 · 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 designQualitative
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

Citations2
Published2018
Admission routes1
Has abstractyes

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