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Record W4213457907 · doi:10.1177/11786329221078803

“I Know How to Advocate”: Parents’ Experiences in Advocating for Children and Youth Diagnosed With Autism Spectrum Disorder

2022· article· en· W4213457907 on OpenAlexaffabout
Joanne Smith-Young, Roger Chafe, Rick Audas, Diana L. Gustafson

Bibliographic record

VenueHealth Services Insights · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAutism spectrum disorderPsychologyAutismDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Parental advocacy is a dynamic process that changes depending on the circumstances and needs of the child and parent. Communication deficits related to an Autism Spectrum Disorder (ASD) diagnosis often necessitate parental advocacy. This study describes how parents and caregivers of children and youth diagnosed with ASD engage in parental advocacy, the challenges they encounter and the advocacy skills they develop. METHOD: We used descriptive exploratory methodology informed by reflexive thematic analysis. The aim of the study was to explore advocacy in parents and caregivers of children and youth diagnosed with ASD. RESULTS: We conducted in-depth, semi-structured interviews with 15 parents of children and youth with an ASD diagnosis living in 4 provinces of Atlantic Canada. The pathway in parents' advocacy journey included: (1) Expressing concerns; (2) Seeking help, assessment, and diagnosis; (3) Acquiring services; (4) Removing barriers; and (5) Developing advocacy skills. CONCLUSIONS: Our findings illustrate the process of parental advocacy, skill development, and the barriers parents encounter in advocating for their children with ASD. Future research might explore how health professionals can support parents' advocacy efforts.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.008
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.004
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 head0.023
GPT teacher head0.324
Teacher spread0.301 · 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 designQualitative
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

Citations41
Published2022
Admission routes2
Has abstractyes

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