MétaCan
Menu
Back to cohort
Record W2918587742 · doi:10.7202/1055890ar

Développement d’un programme innovant pour mieux soutenir les familles vivant avec un enfant présentant une déficience intellectuelle ou un trouble du spectre de l’autisme : s’inspirer des expériences des milieux communautaires

2019· article· fr· W2918587742 on OpenAlexaffvenueabout
Élise Milot, Marie Grandisson, Anne-Sophie Allaire, Charlène Bédard, Martin Caouette, Myriam Chrétien-Vincent, Justine Marcotte, Sébastien Moisan, Sylvie Tétreault

Bibliographic record

VenueService social · 2019
Typearticle
Languagefr
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Au Québec, les besoins de répit et de soutien des familles ayant un enfant présentant une déficience intellectuelle (DI) ou un trouble du spectre de l’autisme (TSA) sont nombreux et persistent. C’est ce qui a motivé un collectif de recherche composé d’acteurs de la ville de Québec à développer OASIS+, une offre de services de soutien à domicile et dans la communauté misant sur l’engagement et la formation d’étudiants universitaires issus des programmes d’ergothérapie et de travail social. Afin de guider le développement d’OASIS+, des entretiens ont été menés avec des représentants d’organismes communautaires de la province de Québec qui ont contribué à élaborer de tels services de soutien. Ils ont permis d’identifier des facilitateurs pour l’implantation d’une offre de services aux familles qui soit pérenne. Cet article présente les résultats de l’analyse de contenu thématique de ces entretiens. Miser sur l’engagement d’étudiants formés semble une avenue pertinente pour favoriser le mieux-être des familles.

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)0.008
metaresearch head score (Gemma)0.008
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 score0.317
Threshold uncertainty score0.630

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.046
GPT teacher head0.335
Teacher spread0.289 · 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

Citations3
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueService socialSame topicFamily and Disability Support ResearchFrench-language works237,207