Recognition of Autism Spectrum Disorder (ASD) symptoms and knowledge about some other aspects of ASD among final year medical students in Nigeria, Sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Earlier studies suggest that knowledge about Autism Spectrum Disorder (ASD) among healthcare workers in Nigeria is low. This present study assessed the knowledge of Nigerian final year medical students about symptoms of ASD and some other aspects of ASD. This is a cross sectional descriptive study that drew a total of seven hundred and fifty-seven (757) final year medical students from ten (10) randomly selected fully accredited medical schools out of a total of twenty-seven (27) fully accredited medical schools in Nigeria. Sociodemographic and Knowledge about Childhood Autism among Health Workers (KCAHW) questionnaires were used to assess knowledge of final year medical students about ASD and obtain demographic information. RESULTS: Only few, 218 (28.8 %) of the 757 final year medical students had seen and participated in evaluation and management of at least a child with ASD during their clinical postings in pediatrics and psychiatry. Knowledge and recognition of symptoms of ASD is observed to be better among this group of final year medical students as shown by higher mean scores in the four domains of KCAHW questionnaire. Knowledge about ASD varies across gender and regions. Misconceptions about ASD were also observed among the final year medical students. CONCLUSIONS: More focus needs to be given to ASD in the curriculum of Nigerian undergraduate medical students, especially during their psychiatry and pediatric clinical postings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".