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Record W4385387261 · doi:10.1016/j.rasd.2023.102208

Making needle procedures comfortable for autistic children: Caregiver perspectives

2023· article· en· W4385387261 on OpenAlexafffundabout
Olivia Dobson, Frank J. Symons, C. Meghan McMurtry

Bibliographic record

VenueResearch in autism spectrum disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcMaster UniversityWestern UniversityMcMaster Children's HospitalUniversity of Guelph
FundersOntario Ministry of Research and InnovationCanada Foundation for Innovation
KeywordsPsychologyThematic analysisAutismAutonomyHealth careAutistic spectrumQualitative researchDevelopmental psychology

Abstract

fetched live from OpenAlex

Background Autistic children are at greater risk for having difficulty undergoing needle procedures and poor management of their needle pain and fear. While there are general clinical practice guidelines (CPGs) available for managing pediatric needle pain and fear, it is unclear whether these strategies are appropriate for autistic children and their caregivers. Method The objective of this study was to explore caregiver perspectives on what is needed for needle procedures to be comfortable and CPGs to be appropriate for them and their autistic child. Twenty Canadian caregivers of autistic children were interviewed, including open-ended questions and ratings of how helpful CPG strategies would be for autistic children. Results Using reflexive thematic analysis , four themes were identified: 1) autistic children’s sense of autonomy is important; 2) external factors impact autistic children’s comfort (e.g., environment, familiarity, healthcare providers); 3) caregivers play a key role by preparing themselves and others before needle procedures and 4) it is essential to tailor CPG strategies to children’s needs. Conclusions Findings indicate that a child and family-centered approach is imperative to making needle procedures comfortable for autistic children. Practical recommendations for healthcare providers and caregivers are provided.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.311
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.399
Teacher spread0.340 · 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
Published2023
Admission routes3
Has abstractyes

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