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Record W4229021212 · doi:10.1111/cch.13015

Neurodisability care in the time of COVID‐19

2022· article· en· W4229021212 on OpenAlexfundno aff
Tomoki Arichi, Jill Cadwgan, Aoife McDonald, Anita Patel, Susie Turner, Sinead Barkey, Daniel E. Lumsden, Charlie Fairhurst

Bibliographic record

VenueChild Care Health and Development · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
FundersMedical Research Council CanadaMedical Research CouncilNational Institute for Health and Care ResearchNIHR Bristol Biomedical Research Centre
KeywordsPandemicCoronavirus disease 2019 (COVID-19)AnxietyHealth careMultidisciplinary approachMental healthPopulationMedicinePsychologyPsychiatryEnvironmental healthDiseasePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic resulted in an unprecedented societal and healthcare global crisis. Associated changes in regular healthcare provision and lifestyle through societal lockdown are likely to have affected clinical management and well-being of children/young people with neurodisability, who often require complex packages of multidisciplinary care. METHODS: We surveyed 108 families of children/young people with severe physical neurodisability and multiple comorbidities to understand how the pandemic had affected acute clinical status, routine healthcare provision, schooling and family mental and social well-being. RESULTS: A significant proportion of families reported missing hospital appointments and routine therapy, with subsequent worsening of symptoms and function. Families additionally described worsening stress and anxiety during the pandemic, regardless of their baseline level of socio-economic deprivation. CONCLUSION: This highlights the profound effect of the COVID-19 pandemic on health and function in young people with severe neurodisabilities and emphasizes the clear need to better understand how to support this vulnerable population moving forwards.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.369
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.306
Teacher spread0.292 · 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 teacher head, 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

Citations6
Published2022
Admission routes1
Has abstractyes

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