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Implantation du Programme d’évaluation, d’intervention et de suivi (EIS) dans un milieu inclusif. Revue internationale de communication et de socialisation, 2(2),74-87

2020· article· fr· W6976460611 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2020
Typearticle
Languagefr
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsSocial impactCultural environmentGender relationsVulnerability (computing)

Abstract

fetched live from OpenAlex

Les programmes d’intervention précoce de qualité préconisent une intervention qui se fait dans les milieux naturels des enfants. Le Programme d’évaluation, d’intervention et de suivi (EIS) (Bricker, 2006) inscrit la participation des milieux au cœur de l’évaluation et de l’intervention auprès du jeune enfant. Une étude réalisée dans un milieu de réadaptation spécialisé en déficience intellectuelle et en troubles du spectre de l’autisme du Québec s’est intéressée à l’implantation de l’EIS dans leurs services. L’article présente les résultats de cette étude en termes d’utilisation et d’appréciation du programme par les intervenants ainsi que les effets perçus par les parents et les éducateurs en milieu de garde. Les résultats montrent que les intervenants voient plusieurs avantages à utiliser l’EIS, mais que des conditions doivent être mises en place pour son appropriation. Les effets retrouvés chez les parents et les milieux de garde témoignent d’une participation plus grande de ces derniers.

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)0.015
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.171
GPT teacher head0.418
Teacher spread0.246 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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