Barriers, facilitators, and experiences of the autism assessment process: A systematic review of qualitative research with health professionals.
Bibliographic record
Abstract
Experiences of professionals diagnosing Autism Spectrum Disorder (ASD) have been significantly under-researched. Around 1%–2% of children in developed countries receive a diagnosis of ASD. Early diagnosis is crucial as delayed diagnosis risks missing the opportunity to receive early interventions which can improve developmental outcomes and quality of life. The purpose of this article was to identify systematically and summarize the experiences and perceptions of health professionals who diagnose ASD. Seven articles were identified, through a systematic search of four databases and the reference lists of identified articles. The articles were critically appraised, and their results were summarized. All articles scored well on the risk of bias assessment. The articles included research from the United States of America, the United Kingdom, Canada, and Belgium. Themes from articles were considered under five topics: barriers, facilitators, diagnostic process, informing of a diagnosis, and postdiagnosis. Clinical implications emphasize the need for clear guidelines, multidisciplinary teams, and a clear process for providing information on the diagnosis and relevant services to parents. Training implications highlight the need to train health professionals on how diagnostic tools and professional judgments can be integrated. Training should also help make professionals aware of the barriers that they may face when diagnosing ASD. Future research is needed to increase the literature on professionals’ experiences of diagnosing ASD and focus on the impact this can have on the health professionals themselves.
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 | 0.042 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".