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Record W2799833947 · doi:10.1177/1049732318770403

“The Weight Is Even Worse Than the Cancer”: Exploring Weight Preoccupation in Women Treated for Breast Cancer

2018· article· en· W2799833947 on OpenAlexafffund
Eva Pila, Catherine M. Sabiston, Valerie H. Taylor, Kelly P. Arbour‐Nicitopoulos

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsCancerBreast cancerMedicineOncologyClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

Cancer-related changes in body weight are problematic given that excess weight is associated with an increased risk of cancer reoccurrence and mortality. The purpose of this qualitative study was to explore the experiences of weight-concerned women treated for early-stage breast cancer. A purposeful sample of women were selected based on criteria for high weight and body image concerns ( n = 11; M age = 65.31 ± 10.96 years). Each participant engaged in a one-on-one semi-structured interview. Five themes were identified: weight concerns contributed to psychological distress, prevalent history of weight cycling and ongoing quest to manage weight, shifting psychological impact of cancer versus weight, perceptions of failure around goal-oriented weight management behaviors, and internalized and explicit social pressures for weight loss in the context of risk reduction. In light of the fundamental challenges of weight management, and the present findings, improving weight-related distress should be a clinical priority to improve the well-being of women in survivorship.

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)0.038
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0090.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.642
GPT teacher head0.683
Teacher spread0.041 · 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 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

Citations18
Published2018
Admission routes2
Has abstractyes

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