MétaCan
Menu
Back to cohort
Record W4320034101 · doi:10.3166/pson-2022-0220

« Le cancer, ce n’est pas juste une histoire de maladie » : l’expérience du cancer du sein à la lumière des métaphores des patientes

2023· article· fr· W4320034101 on OpenAlexaff
Alexandra Guité‐Verret, M. Vachon

Bibliographic record

VenuePsycho-Oncologie · 2023
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

L’objectif de cette étude qualitative est de mieux comprendre l’expérience du cancer du sein à partir d’une analyse des métaphores présentes dans les récits de femmes atteintes d’un cancer du sein. Les blogs de deux femmes ont été sélectionnés puis analysés selon une approche phénoménologique interprétative. Notre analyse met de l’avant trois métaphores à la lumière desquelles ces femmes semblaient vivre et saisir leur expérience du cancer du sein : le corps comme champ de bataille médical, le corps morcelé et le chemin de la maladie. Ces résultats indiquent, d’une part, la violente atteinte des barrières du corps et du sujet dans la maladie cancéreuse, d’autre part, la nécessité pour le sujet d’effectuer un travail psychique pour intégrer cette expérience à son existence, au lieu de la combattre. L’étude apporte aussi des éléments de réflexions sur les métaphores en tant que vecteurs de sens de l’expérience du 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)0.004
metaresearch head score (Gemma)0.009
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 score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.013
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.003
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 head0.082
GPT teacher head0.414
Teacher spread0.332 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePsycho-OncologieSame topicHealth, Medicine and SocietyFrench-language works237,207