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Record W3083554021 · doi:10.46278/j.ncacn.20170910

L'apport de l'Association québécoise des neuropsychologues à la pratique clinique : évolution depuis sa création et défis futurs

2017· article· fr· W3083554021 on OpenAlexaffvenueabout
William Aubé, Simon Charbonneau, Jean‐Pierre Chartrand, Frédérique Escudier, Simon Lemay, Édith Léveillé, Elisabeth Perreau-Linck

Bibliographic record

VenueNeuropsychologie clinique et appliquée · 2017
Typearticle
Languagefr
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à MontréalInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier de l’Université de MontréalHôpital du Sacré-Cœur de MontréalInstitut universitaire en santé mentale de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

L'objectif du présent article est de dresser un portrait de l'Association québécoise des neuropsychologues (AQNP) et de la pratique de la neuropsychologie au Québec. Fondée en 2012 dans la conjoncture de la mise en application du projet de loi 21 (PL21), l'AQNP connaît dès ses débuts un essor fulgurant auprès des neuropsychologues québécois. La mission de l'AQNP, visant à favoriser le développement de la neuropsychologie au Québec, s'est édifiée selon six objectifs principaux, tous motivés par les besoins des neuropsychologues et de la population. C'est autour de ces objectifs que cet article est articulé en exposant, notamment, les réalisations, les activités, les services aux membres ainsi que les projets en cours et à venir de l'AQNP. Les auteurs proposent enfin des réflexions entourant les défis anticipés pour l'AQNP, mais aussi ceux liés au maintien et au développement de la pratique de la neuropsychologie au Québec.

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)0.007
metaresearch head score (Gemma)0.018
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0050.006
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.116
GPT teacher head0.449
Teacher spread0.333 · 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
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
Published2017
Admission routes3
Has abstractyes

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