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Record W4393155220 · doi:10.3389/fresc.2024.1308062

Alignment of Canada's COVID-19 policy response with barriers and facilitators for coping reported by caregivers of youth with developmental delays, disorders, and disabilities

2024· article· en· W4393155220 on OpenAlexafffundabout
Anna Katalifos, Mayada Elsabbagh, Afiqah Yusuf, Sakiko Yamaguchi, Julie Scorah, Nicola Wright, Mandy Steiman, Andy Shih, Keiko Shikako‐Thomas

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéPublic Health AgencyPublic Health Agency of Canada
KeywordsThematic analysisPandemicCoping (psychology)Mental healthPsychologyPublic healthCoronavirus disease 2019 (COVID-19)MedicineNursingPsychiatryQualitative researchSociologyDisease

Abstract

fetched live from OpenAlex

Introduction: The UNICEF-WHO Global Report on Developmental Delays, Disorders, and Disabilities is an ongoing initiative aimed at increasing awareness, compiling data, providing guidance on strengthening health systems, and engaging country-level partners. Data from its caregiver survey assessing impacts of the COVID-19 pandemic showed that half of youths with developmental delays and disabilities (DDDs) and their caregivers struggled to cope, with a significant portion reporting a lack of supports and difficulty managing the worsening of the child's symptoms in isolation. Governments created service strategies supporting vulnerable groups. Little is known about the alignment between COVID-19 policies for persons with disabilities and their lived experiences. Contextualizing caregivers' experiences can promote the development of tailored public supports for these families following a public health crisis. Methods: Online survey data were collected from June-July 2020, leading to a convenience sample of caregivers of youth with DDDs across Canada. Respondents answered two open-ended questions regarding challenges and coping strategies during the pandemic. We conducted a thematic analysis of responses using inductive coding on NVivo software. Overarching codes derived from the dataset were contextualized using an analysis of provincial policies published during the pandemic. Parallels with these policies supported the exploration of families' and youths' experiences during the same period. Results: = 576) participants answered open-ended questions. Barriers to coping included family mental health issues, concerns about the youths' regression, challenges in online schooling, limited play spaces, and managing physical health during quarantine. Environmental barriers encompassed deteriorating family finances, loss of public services, and a lack of accessible information and supports. In contrast, caregivers reported coping facilitators, such as family time, outdoor activities, and their child's resilience. Environmental facilitators included community resources, public financial supports, and access to telehealth services. Few COVID-19 policies effectively addressed caregiver-identified barriers, while some restrictions hindered access to facilitators. Conclusion: Prioritizing needs of families of youths with DDDs during public health emergencies can significantly impact their experiences and mental health. Enhancing financial benefits, providing telehealth services, and creating inclusive public play spaces are priority areas as we navigate the post-pandemic landscape.

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)0.003
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.491

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.325
Teacher spread0.303 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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