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Record W562990947 · doi:10.1093/pch/20.5.e20

Factors influencing autism spectrum disorder screening by community paediatricians

2015· article· en· W562990947 on OpenAlexaffabout
Angie Ip, Lonnie Zwaigenbaum, David Nicholas, Raphael Sharon

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of CalgaryUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsAutism spectrum disorderAutismFocus groupMedicineQualitative researchFamily medicineClinical psychologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: In most cases, autism spectrum disorders (ASD) can be reliably diagnosed at two to three years of age. However, Canadian data reveal a median age at diagnosis of approximately four years. OBJECTIVE: To examine general paediatricians' practices regarding ASD screening and identify factors that influence decisions regarding the use of ASD screening tools. METHODS: Using a qualitative inquiry-based interpretive description approach, 12 paediatricians from four practice groups participated in four focus groups and one individual interview. These were conducted using semistructured interviews, digitally recorded and transcribed verbatim. RESULTS: Five main domains of themes were identified related to screening tool use: benefits; needs not addressed; elements that limit utility; elements that encourage utility; and implementation challenges. Factors influencing practice included availability of time, comfort with screening tool use, previous use and knowledge about specific tools. Systemic factors included knowledge and access to community resources, as well as the ability to provide support to the child and family. CONCLUSION: The results from the present study identified important factors that influence paediatric practice in ASD screening. As screening tools improve, it will be important to examine the implementation and effectiveness of screening tools and strategies for increased uptake. Future research will also need to attend to the practical needs of physicians and communities in the aim of earlier diagnosis and rapid access to interventional resources.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.003
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 head0.061
GPT teacher head0.318
Teacher spread0.258 · 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.

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

Citations21
Published2015
Admission routes2
Has abstractyes

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