Socio-economic and Demographic Factors Associated with Adaptive Behavioural Functions of Children Diagnosed with Intellectual Disability in Ethiopia
Bibliographic record
Abstract
The socio-economic and demographic factors can influence on the planning and implementation of intervention service for children with intellectual disability. The present study aims to assess the socio-economic and demographic factors associated with adaptive functions of children diagnosed with Intellectual Disability in Ethiopia. 160 children with intellectual disability were included in this cross-sectional study. Vinland Adaptive Behavioural Scale II was administered to assess adaptive behaviour of children diagnosed with intellectual disability. The current study's participants performed poorly in all adaptive behaviour domains and sub-domains. In comparison to younger children, older youngsters scored lower on adaptive behaviour. This indicated that domains of adaptive behavioural functions were linked with age negatively: age and communication (-0.28), age and daily living (-0.22), age and socialization (-0.23); and adaptive composite score and age (-0.3). There was no significant correlation between all domains and subdomain of adaptive behaviour and educational status and gender. In contrast, there was a significant relationship between parents' or caregivers' marital status and all domains and subdomains of adaptive behavioural functions in children with intellectual disability. Those children who were living with married couples perform better than those who were living with single mother, divorced and widowed.Generally, in underdeveloped nations like Ethiopia, description of socio-economic and demographic correlates of intellectual disabilities in children diagnosed with intellectual disability is crucial for designing a meaningful and useful policy and strategy for early identification and intervention of children with intellectual disability.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".