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Record W2492917444 · doi:10.1111/jar.12274

The Benefits of Supporting the Autonomy of Individuals with Mild Intellectual Disabilities: An Experimental Study

2016· article· en· W2492917444 on OpenAlexaff
Julie Pelletier, Mireille Joussemet

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2016
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversité de MontréalCégep Marie-Victorin
Fundersnot available
KeywordsAutonomyIntellectual disabilityPsychologyPersonal autonomyAnxietyLearning disabilityDevelopmental psychologyPopulationSocial psychologyClinical psychologyGerontologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The benefits of autonomy support with the general population have been demonstrated numerous times. However, little research has been conducted to verify if these benefits apply to people with special needs. METHODS: The goal of the study was to examine whether autonomy support (AS) can foster the sense of autonomy of people with a mild intellectual disabilities (MIDs) and improve their experience while engaging in an important but unpleasant learning activity. This experiment compares the effects of two contexts: with and without AS. All participants (N = 51) had a mild intellectual disability and were recruited from rehabilitation centres. RESULTS: Compared to participants in the control group, participants in the AS group tended to experience greater autonomy satisfaction and tended to perceive more value to the activity. They were also significantly more engaged in it, and they experienced a steeper decrease in anxiety over time. CONCLUSIONS: This study suggests that the benefits of AS extend to individuals with mild intellectual disability.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.135
GPT teacher head0.409
Teacher spread0.274 · 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 designNon-randomized trial
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

Citations39
Published2016
Admission routes1
Has abstractyes

Explore more

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