Barriers to the early detection and intervention of children with autism spectrum disorders: A literature review
Bibliographic record
Abstract
Background and objective: Autism spectrum disorder (ASD) is a lifelong developmental disability that affects how individuals communicate and interact with others. A reliable diagnosis of ASD can be made within the first 24 months of a child’s life, but ASD is usually diagnosed late. Late diagnosis contributes to missed opportunities to provide early intervention services and improve long-term outcomes. The purpose of this project was to identify barriers to early detection and intervention of ASD faced by parents, other caregivers, and health care professionals.Methods: A literature review was conducted. CINAHL, Medline, and PsychINFO databases were used to search for relevant articles. Ten articles that met the inclusion criteria were selected and data from these articles were summarized in a data extraction table and themes were identified.Results: Five main barriers that prevent early diagnosis and intervention of children with ASD were identified. These barriers were lack of knowledge, social stigma, dismissal of parents’ first concerns by healthcare providers, barriers to ASD screening, and access to ASD services.Conclusions: The results of this literature review will inform the development of an educational guide for parents and other caregivers to promote their knowledge and awareness about ASD in children.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".