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Enregistrement W4322720019 · doi:10.1177/13623613231154729

Variable patterns of daily activity participation across settings in autistic youth: A latent profile transition analysis

2023· article· en· W4322720019 sur OpenAlexafffund
Yun‐Ju Chen, Eric Duku, Anat Zaidman‐Zait, Péter Szatmári, Isabel M. Smith, Wendy J. Ungar, Lonnie Zwaigenbaum, Tracy Vaillancourt, Connor M. Kerns, Teresa Bennett, Mayada Elsabbagh, Ann Thompson, Stelios Georgiades

Notice bibliographique

RevueAutism · 2023
Typearticle
Langueen
DomaineNeuroscience
ThématiqueAutism Spectrum Disorder Research
Établissements canadiensMcGill UniversityUniversity of OttawaUniversity of AlbertaUniversity of TorontoDalhousie UniversityUniversity of British ColumbiaHospital for Sick ChildrenCentre for Addiction and Mental HealthMcMaster University
Organismes subventionnairesCanadian Institutes of Health ResearchKids Brain Health NetworkAlberta InnovatesAlberta Innovates - Health SolutionsSinneave Family FoundationAutism Speaks
Mots-clésPsychologyAutismDevelopmental psychologyActivities of daily livingClubIndependent livingGerontologyMedicinePsychiatry

Résumé

récupéré en direct d'OpenAlex

Participation in daily activities is often linked to functional independence and well-being, yet individual variability in participation and factors associated with that variation have rarely been examined among autistic youth. We applied latent profile analysis to identify subgroups of youth based on parent-reported activity participation frequency at home, school and community, as well as associations with youth characteristics, family demographics and environmental supportiveness among 158 autistic youth (aged 11–14 years at baseline). Three-, three- and two-profile solutions were selected for home, school and community settings, respectively; the most prevalent profiles were characterized by frequent home participation (73%), low participation in non-classroom activities at school (65%) and low community participation, particularly in social gatherings (80%), indicating participation imbalance across settings. More active participation profiles were generally associated with greater environmental support, higher cognitive and adaptive functioning and less externalizing behaviour. Latent transition analysis revealed overall 75% stability in profile membership over approximately 1 year, with a different home participation profile emerging at the second time-point. Our findings highlighted the variable participation patterns among autistic youth as associated with individual, family and environmental factors, thus stressing the need for optimizing person–environment fit through tailored supports to promote autistic youth’s participation across settings. Lay abstract What people do or engage in in their daily lives, or daily life participation, is often linked to their state of being happy and healthy, as well as potential for living independently. To date, little research has been conducted on daily activity participation by autistic youth at home, at school or in the community. Learning more about individual differences in participation levels and what might influence them can help to create custom supports for autistic youth and their families. In this study, 158 caregivers of autistic youth were asked how often their children took part in 25 common activities at two assessments, about one year apart. The analysis showed three profiles for each of the home and school settings and two profiles for the community setting. These profiles reflected distinct patterns in how often autistic youth took part in various daily activities, particularly in doing homework, school club activities and community gatherings. Most autistic youth were in profiles marked by often taking part at home but less often at school and in the community, and about three-fourths of them tended to stay in the same profile over time. Autistic youth with limited participation profiles were more likely to have lower scores on measures of cognitive ability and daily life skills and more challenging behaviour, and faced more barriers in their environment. These findings show how important it is to think about each autistic person’s strengths and weaknesses, and changing needs, to better support their daily life participation.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,669
Score d'incertitude au seuil0,615

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,003
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,049
Tête enseignante GPT0,336
Écart entre enseignants0,287 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2023
Routes d'admission2
Résumé présentoui

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