Knowledge and Awareness of Autism Spectrum Disorder Among Teachers in Ekiti State, Nigeria
Bibliographic record
Abstract
This is an investigation of the knowledge and awareness of Autism Spectrum Disorder among secondary school teachers in Ekiti State, Nigeria. It is a school-based cross-sectional study using a multi-stage sampling method to select local government areas, secondary schools, and participants for the study. A total of 107 teachers selected from 21 secondary schools in 2 local government areas participated in the study. A Survey of Knowledge of Autism Spectrum Disorder (ASK-ASD) was used to assess knowledge while an ordinary awareness questionnaire was used to assess the teachers' awareness of the disorder. Using Pearson correlation coefficient and one-way Analysis of Variance to test five hypotheses, results showed no significant relationship between knowledge of Autism Spectrum Disorder and participants having a family or friend with the disorder (r (105) = -.113 p>.05). Finding also revealed a significant relationship between knowledge of Autism and prior training on Autism (r(107) = -.266 p<.05). Age has a significant influence on knowledge of the disorder (F (2, 98) = 4.29 p<.05) but school type (F (2, 104) = 2.506 p>.05) and teaching experience (F (2,103) = 1.971 p>.05) do not have a significant influence on knowledge of Autism. The result further shows that 96.2% of the participants were aware of the disorder while 3.8% were unaware of it. It was recommended that secondary school teachers be equipped with information about Autism Spectrum Disorder through periodic seminars, workshops, and conferences.
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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 | 0.001 |
| 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) | 0.001 | 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".