Norms of the<scp>A</scp>ttitudes<scp>T</scp>oward<scp>I</scp>ntellectual<scp>D</scp>isability<scp>Q</scp>uestionnaire
Bibliographic record
Abstract
BACKGROUND: The Attitudes Toward Intellectual Disability Questionnaire (ATTID) has demonstrated good psychometric qualities for measuring the attitudes of different groups of adults in the general population toward intellectual disability (ID). A significant advantage of the ATTID is that it addresses the concept of attitudes using a three-dimensional model (affective, cognitive and behavioural). To our knowledge, there are no normative data published regarding attitudes toward ID on general population-based samples. METHODS: The sample of 1605 men and women was stratified to be representative of the general adult population of Quebec. The ATTID was administered by phone interview through an independent survey firm. RESULTS: Normative data are presented as percentile scores associated with the raw score of the ATTID by gender and age categories. Analysis of the variance yielded significant differences in attitude by gender and age. The directions and the strength of these associations vary according to each of the five factors used to define attitudes. CONCLUSION: These norms will provide an essential tool to compare different groups and assess the effectiveness of various public campaigns to encourage more positive attitudes towards persons with ID. These norms would also allow international comparisons.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".