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Record W2217395853 · doi:10.7202/1073493ar

Traumatisme craniocérébral

2020· article· fr· W2217395853 on OpenAlexaffvenue
Hélène Lefebvre, Marie‐Josée Levert

Bibliographic record

VenueFrontières · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyArtPsychology

Abstract

fetched live from OpenAlex

Le traumatisme craniocérébral est une expérience déchirante pour les familles, mais aussi pour les professionnels de la santé qui vivent de l’impuissance face à la souffrance de la personne ayant le traumatisme craniocérébral et de ses proches. Le processus de deuil de la personne telle qu’elle était avant le traumatisme crânien et celui de leur vie antérieure est certes souffrant, mais s’accompagne d’un potentiel d’apprentissage et de croissance, tant pour les familles que les professionnels de la santé impliqués auprès d’elles. Une relation reposant sur le partage réciproque des savoirs et des expertises dans un rapport égalitaire est garante d’une relation satisfaisante, malgré une atmosphère chargée d’émotions. Le partenariat constitue en effet une stratégie intéressante qui permet de faire de ce moment de souffrance une expérience positive pour les familles et les professionnels de la santé.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.069
GPT teacher head0.368
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2020
Admission routes2
Has abstractyes

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