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Enregistrement W4390079034 · doi:10.1017/s1355617723007890

14 Changes in Service Delivery Models for Children with Neurodevelopmental Disorders During the Covid-19 Pandemic

2023· article· en· W4390079034 sur OpenAlexaff
Buse Bedir, Sunny Guo, Brian Katz, Sarah J. Macoun

Notice bibliographique

RevueJournal of the International Neuropsychological Society · 2023
Typearticle
Langueen
DomainePsychology
ThématiqueFamily and Disability Support Research
Établissements canadiensSurrey Place CentreUniversity of Victoria
Organismes subventionnairesnon disponible
Mots-clésPandemicPsychological interventionEthnic groupService delivery frameworkCoronavirus disease 2019 (COVID-19)MedicineMental healthIndigenousPsychologyService (business)Family medicinePsychiatryPolitical scienceBusiness

Résumé

récupéré en direct d'OpenAlex

Objective: With the onset of the COVID-19 pandemic, many families face barriers in accessing critical services for their children. However, there is a disproportionate impact on families of children with Neurodevelopmental Disorders (NDDs), particularly those who are dependant on receiving regular services. The current study investigated how service delivery has changed for children and families with NDDs during the COVID-19 pandemic, to identify which groups are most at risk for service disruption and negative outcomes, and to provide actionable recommendations for community agencies that provide early interventions for future pandemics. Participants and Methods: Data was collected in the fall and winter of 2020/2021 during the Covid-19 pandemic. Families were recruited from a local service provider in British Columbia whose Early Years Support services delivery model was changed to online delivery during the pandemic. Children had a diagnosis of NDD or were on the waitlist for an assessment. Overall, 26 families participated in a semi-structured interview that asked about their experiences of receiving services for their children during the pandemic. Of these families, 20 subsequently completed online questionnaires that asked about their parenting stress levels and their children’s behaviour throughout the pandemic. Families of a range of compositions were drawn from different ethnicities (30% white, 25% South Asian, 20% Filipino, and the remaining 5% identified as Indigenous, African or East Asian). The mean age of children was 3.80 years (SD =0.72). Results: From the survey, we found that 58% of parents reported higher than average levels of mental health and behavioural challenges in their children during the Covid-19 pandemic. In addition, 45% of parents reported higher than average parenting stress levels. Qualitative interview data indicated that most parents reported positive experiences with receiving services during the Covid-19 pandemic and reported feeling supported even with social distancing measures. However, families also reported increased stress levels and isolation, particularly those who have children with Autism Spectrum Disorder, who rely on early funding (06 years) and early services. One of the themes that emerged from parents who were on the waitlist to receive an assessment was that wait times around assessments were very long, which contributed to parent stress levels. Parents also reported concerns around wait times to access services and difficulty of accessing online services due to internet and connection issues. Conclusions: The current study identified central themes of stressors and barriers experienced by families and children with NDDs in service delivery. Overall, parents reported satisfaction in changes in service delivery in most ways; however, they also reported stresses and barriers that included wait times, increased isolation, and accessing online services. Actionable steps to reduce family stress include better communication between service providers and families for wait times, and more variability in appointment times. Specific recommendations for current and future pandemics will be expanded on in the poster.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,317
Score d'incertitude au seuil0,631

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0020,001
Science ouverte0,0020,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,090
Tête enseignante GPT0,362
Écart entre enseignants0,272 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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