Perception and Acceptability of Regular Education Teachers Towards Inclusion of Children with Intellectual Disability in Calabar, Cross River State, Nigeria
Bibliographic record
Abstract
Access to education has been a challenge to individuals with intellectual disability (ID) in Cross River State, Nigeria, as a result of limited schools that accept these individuals. This study consequently investigated the knowledge and perception of regular teachers about children with ID and their level of acceptance into the regular classroom. Two hundred (200) teachers in public primary schools in Calabar municipal were randomly selected for the study. A descriptive research design was adopted. “Teachers’ Knowledge, Perception and Acceptability of Teachers towards Children with Intellectual Disability (TKPATCID)” was used as an instrument for data collection. Data was analyzed using descriptive statistics such as frequency count, simple percentage standard deviation, and mean scores. Findings showed that the knowledge of regular teachers about children with ID is very low. The majority of the regular teachers have negative perceptions of children with ID. Similarly, the majority of the respondents were of the opinion that children with ID should not be accepted alongside their non-disabled counterparts in the classroom. It was therefore recommended, among others, that awareness of the nature of ID be created. Regular in-service training should be organized for regular education teachers by the government in order to properly equip them with relevant and up-to-date knowledge of children with ID.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".