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Record W4391690692 · doi:10.1111/jar.13207

The Family Game to support parents with intellectual disability in managing challenging behaviours: A replication

2024· article· en· W4391690692 on OpenAlexafffund
Joany Tremblay, Élise Douard, Marc J. Lanovaz

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2024
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de QuébecUniversité de Montréal
FundersUniversité de Montréal
KeywordsReplication (statistics)Intellectual disabilityBaseline (sea)PsychologyDevelopmental psychologyTypically developingMultiple baseline designApplied psychologyMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Although many parents with intellectual disability (ID) demonstrate good parenting practices, some parents experience difficulties in managing challenging behaviours. One potential solution to this issue involves using The Family Game, a program designed to teach parents with ID how to manage challenging behaviours in their child. AIMS: The purpose of our study was to conduct an independent replication of an investigation that had been performed by the developer of the program. MATERIALS & METHODS: We used a multiple baseline design to examine the effects of The Family Game on the behaviour of two parents with ID who had a 3-year-old child. RESULTS: Similarly to the original study, our results indicate that The Family Game improved the use of effective parenting strategies during role play, but that these gains failed to generalise to real-life settings. CONCLUSION: The study further supports the necessity of adding novel strategies to the game to better promote generalisation.

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)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.161
GPT teacher head0.442
Teacher spread0.281 · 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 designObservational
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
Published2024
Admission routes2
Has abstractyes

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