MétaCan
Menu
Back to cohort
Record W3116196013 · doi:10.3166/pson-2020-0136

Les effets narratifs de l’art pictural auprès du proche aidant en psycho-oncologie

2020· article· fr· W3116196013 on OpenAlexaff
M. Colas, A. Santarpia, P. Cannone, C. Bonnet

Bibliographic record

VenuePsycho-Oncologie · 2020
Typearticle
Languagefr
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Objectif : Cette étude qualitative vise à décrire les effets narratifs d’un protocole d’accompagnement psychooncologique s’appuyant sur les productions picturales d’une proche aidante (Mme Rose, 70 ans) accompagnant son mari atteint d’un cancer incurable. Matériel et méthode : Il s’agit d’un protocole autour de la figuration picturale de la proche aidante composé de quatre étapes (temps) : l’entretien préliminaire (T1), une première rencontre autour de la création picturale (T2), une seconde rencontre d’approfondissement autour de la création picturale (T3), l’entretien final (T4). Nous avons utilisé le logiciel T-LAB 9.1.3 pour le calcul des associations de mots (cooccurrences) et réalisé une interprétation du récit selon l’approche humaniste/existentielle. Résultats : Ce dispositif de recherche a permis de montrer les effets narratifs avant et après le travail artistique sur les problématiques psychiques inhérentes au vécu du proche aidant et notamment sur l’angoisse de mort. La dimension romantique du récit semble pouvoir supporter la narration tragique de Mme Rose. Conclusion : Le « travail psychique avec la production picturale » peut permettre au proche aidant de mettre en figure l’amour et la mort à travers une narration personnelle empruntant à l’univers narratif du romantisme ses enjeux existentiels.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.083
GPT teacher head0.338
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePsycho-OncologieSame topicArt Therapy and Mental HealthFrench-language works237,207