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Record W3159044071 · doi:10.7202/1076537ar

Jargon médical : marqueur et médiateur du vécu des couples en procréation médicalement assistée

2021· article· fr· W3159044071 on OpenAlexaffvenueabout
Gabrielle Pelletier, Raphaële Noël

Bibliographic record

VenueEnjeux et société Approches transdisciplinaires · 2021
Typearticle
Languagefr
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Au Canada, l’utilisation de la procréation médicalement assistée (PMA) augmente annuellement. Lorsque les couples racontent leur expérience en clinique de fertilité, leur récit est marqué par une utilisation significative du jargon médical (JM). S’inscrivant dans un projet portant sur les acteurs du don d’ovules, cette recherche qualitative exploratoire vise à décrire et comprendre l’usage du JM par les couples ainsi que les liens possibles avec leur vécu de la PMA. Les entrevues semi-dirigées de trois couples ont été sélectionnées dans un échantillon de huit couples en raison de leur usage marqué du JM. Une méthodologie qualitative inductive alliant cinq paliers d’analyses a été construite afin d’analyser les contenus manifestes et latents des entretiens. L’élaboration de la métaphore d’un voyage en navette spatiale comme moyen d’intégrer et de conceptualiser les résultats met en évidence l’intensité et la technicité du parcours en PMA de ces couples ainsi que différentes dynamiques conjugales.

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)0.006
metaresearch head score (Gemma)0.012
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 score0.144
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.017
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.041
GPT teacher head0.373
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

Citations1
Published2021
Admission routes3
Has abstractyes

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