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Record W4308409588 · doi:10.36834/cmej.75603

Cartographier en 3D avec MapIt : une plus-value pour un parcours de professionnalisation selon la perspective étudiante

2022· article· fr· W4308409588 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Medical Education Journal · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicInformation Technology and Learning
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsProfessionalizationPerspective (graphical)Adaptation (eye)Value (mathematics)Element (criminal law)HumanitiesPsychologySociologyComputer sciencePolitical scienceArtArtificial intelligenceSocial scienceLaw

Abstract

fetched live from OpenAlex

Énoncé des implications de la recherche Durant la pandémie, l’application MapIt a été intégrée dans un programme d’ergothérapie pour soutenir l’apprentissage à distance de l’adaptation de l’environnement bâti. MapIt permet de cartographier des pièces d’un domicile, puis d’en générer un modèle en 3D pour la visualisation et la prise de mesures virtuelles. Les étudiantes expriment que le recours à MapIt durant leur formation mène à incarner les rôles attendus d’une ergothérapeute. Pour inspirer d’autres bonnes idées pédagogiques, cet article présente comment MapIt peut soutenir l’apprentissage en situations authentiques, un élément clé d’un parcours de professionnalisation, s’approchant des réalités vécues par les personnes patientes, clientes ou intervenantes

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.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0590.000

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 head0.006
GPT teacher head0.298
Teacher spread0.292 · 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