MétaCan
Menu
Back to cohort
Record W4229084040 · doi:10.1016/j.bodyim.2022.04.012

Body image in women diagnosed with breast cancer: A grounded theory study

2022· article· en· W4229084040 on OpenAlexafffundabout
Jennifer Brunet, Jenson Price, Cheryl Harris

Bibliographic record

VenueBody Image · 2022
Typearticle
Languageen
FieldMedicine
TopicWomen's cancer prevention and management
Canadian institutionsInstitut du Savoir MontfortOttawa HospitalMontfort HospitalUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrounded theoryBreast cancerIntrapersonal communicationPsychologyQualitative researchInterpersonal communicationGynecologySocial psychologyCancerMedicineInternal medicineSociologySocial science

Abstract

fetched live from OpenAlex

Using a Straussian grounded theory methodology, we explored the meaning women attribute to body image and how they understand their breast cancer experience as influencing their body image to develop a grounded theory of body image for women diagnosed with breast cancer. Interviews were conducted with 27 women who had completed treatment for breast cancer in Canada. Data were analyzed through a process of open, axial, and selective coding using constant comparison techniques and memo-writing. A grounded theory of body image for women diagnosed with breast cancer was developed around the core category of body image: what it means to women, which was underpinned by six themes and 17 subthemes. This theory explains how women diagnosed with breast cancer define body image and illustrates intrapersonal and interpersonal factors that can undermine or support their body image, along with strategies they used to manage their body image. This theory can guide research and practice aimed at enhancing body image and minimizing its consequences for women diagnosed with breast cancer.

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)0.013
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 score0.017
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0060.005
Scholarly communication0.0030.002
Open science0.0020.003
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 head0.007
GPT teacher head0.270
Teacher spread0.264 · 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

Citations70
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueBody ImageSame topicWomen's cancer prevention and managementFrench-language works237,207