A cross‐sectional multivariate analysis of children's attitudes towards disabilities
Bibliographic record
Abstract
BACKGROUND: Past research has shown that children can be biased against peers with disabilities, but the association of attitudes with gender, age and disability preferences, as well as interactions between these variables, are unclear. The objectives of this study were to examine these issues in a cross-sectional, split-plot study to clarify: (1) if elementary school children's attitudes towards peers with disabilities are related to age, gender and type of disability; (2) if interactions between these variables exist; and (3) if convergent validity could be achieved across three theoretically linked dependent variables. METHODS: One hundred elementary school children between 4 and 10 years old were assessed for attitudes towards target children with no disability, a physical or an intellectual disability, and a combined intellectual/physical disability. Measures were completed in an interview format. RESULTS: Attitudes towards a target child with physical disabilities and a target child without disabilities did not differ. There was a significant interaction for age and disability. Attitudes towards target children with intellectual and intellectual/physical disabilities were negatively biased, and were negatively associated with age. Results were consistent across measures except for a main effect of gender in one measure and a gender by age interaction in another. CONCLUSIONS: Children's attitudes appear to be associated with several factors, including age and the presence or absence of disability. Gender differences in attitudes may be because of gender-based response biases rather than disability biases. Because of the multifaceted nature of childhood attitudes, cross-sectional designs with several dependent and independent variables provide an opportunity to examine consistency of results across measures and potential interactions between factors that may not be uncovered when variables are examined in isolation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".