Validation of the Persian version of the attitudes toward intellectual disability
Bibliographic record
Abstract
Abstract Background Attitudes toward individuals with intellectual disability (ID) are the most important factor affecting their social integration and can cause them to experience a sense of achievement or discrimination. The present study aimed to evaluate the latent factor structure and validity of the Persian version of the Attitudes toward Intellectual Disability (ATTID) Short‐Form questionnaire. Methods The latent factor structure of the Persian version of the ATTID Short‐Form was established in a convenient sample of the general population (N = 280) in Iran. The structural validity and temporal reliability, internal consistency and confirmatory factor analysis were evaluated. Data analysis was done with SPSS v23 Windows edition and R v4.2.1. Results The Persian version of the ATTID Short‐Form was shown to have a five‐factor structure: discomfort, knowledge of capacity, interaction, sensitivity and knowledge of cause. The structure was appropriately fit according to the fit indices (χ2(485) = 530.12), (P‐value = 0.077). All the subscales had good temporal reliability. Conclusions Findings suggest that the Persian version of the Short‐Form of ATTID is a brief, valid and reliable measure that can be used in research and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".