Awareness about Autism among Primary Healthcare Providers in Oman: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Many developing countries such as Oman are marred with the rising tide of children with autism and the lack of specialized services for these children. Within existing compartmentalized and centralized health care organizations, the general practitioners (GPs) are likely to serve as the first level of contact relevant for diagnosis and referral for remedial services. This study aims to explore the awareness of autism among GPs in Oman on issue pertinent to etiology, signs and symptoms, perceived correlates, as well as the consequence of having autism. Related to this is to examine whether years of medical practice would invariably influence GPs’ awareness. METHODS: This cross-sectional study was conducted among GPs (n=113) working at primary healthcare centers (PHC) during September 2013 to February 2014 in Muscat, the capital of Oman.RESULTS: The GPs appear to have suboptimal awareness of etiological factors relevant for the development of autism, its common signs and symptoms, perceived correlates, as well as the social dimension. Number of years in practice has little bearing on awareness.CONCLUSION: Empirical evidence has unequivocally indicated that although there is no known ‘cure’ for autism, early identification and early intervention tend to better the quality of life for children with autism. This means GPs in Oman should be fitted with adequate awareness of such population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".