MétaCan
Menu
Back to cohort
Record W3197962551 · doi:10.1080/17482631.2021.1971597

The incurable metastatic breast cancer experience through metaphors: the fight and the unveiling

2021· article· en· W3197962551 on OpenAlexaff
Alexandra Guité‐Verret, Mélanie Vachon

Bibliographic record

VenueInternational Journal of Qualitative Studies on Health and Well-Being · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsEthica (Canada)Quebec Network for Research on AgingUniversité du Québec à Montréal
Fundersnot available
KeywordsBreast cancerMetaphorContext (archaeology)Meaning (existential)BattleMetastatic breast cancerCancerMedicinePsychologyPsychotherapistInternal medicineHistoryLinguistics

Abstract

fetched live from OpenAlex

Purpose: War metaphors are omnipresent in public and medical discourse on cancer . If some studies suggest that cancer patients may view their experiences as afight, few studies focus on the metaphors that patients create from their subjective experiences. The aim was to better understand the experience of four women with incurabale metastatic breast cancer from the metaphors they used in personal cancer blogs.Methods: An interpretive phenomenological analysis (IPA) was used to analyze these women's experience and metaphors of cancer.Results: Two metaphors carried the meaning of metastatic breast cancer experience: the fight and the unveiling. The results show that the war metaphor had a unique meaning for the bloggers who lived with incurable breast cancer: they revealed the difficulty of fighting cancer and eventually collapsing in battle, although a renewed look at life had developed in parallel to their struggle. The bloggers thus tried to lift the veil on this complex experience.Conclusion: The results highlight the need for women with metastatic breast cancer to be able to tell and share their experience in a supportive context and to reinvest the war metaphor in order to express themselves in a more authentic way.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.368
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.478
Teacher spread0.392 · 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.

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

Citations25
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Qualitative Studies on Health and Well-BeingSame topicLanguage, Metaphor, and CognitionFrench-language works237,207