Psychometric properties of the Disability Identity Development Scale: Confirmatory factor and bifactor analyses.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: This work provides comprehensive analyses targeting the factor structure and dimensionality of the Disability Identity Development Scale (DIDS). In Forber-Pratt et al., 2020, disability was defined broadly to include individuals with visible or hidden disabilities across many disability groups (i.e., physical, intellectual, learning, or chronic illness). RESEARCH METHOD/DESIGN: = 1,126) ranging in age from 18-78 years. Confirmatory factor analytics (CFA) including traditional CFA, and bifactor confirmatory analyses were used to examine the dimensionality and structure of the DIDS. RESULTS: Traditional CFA provided lack of evidence in support of the oblique four-factor structure previously reported. Bifactor confirmatory analysis revealed items on the DIDS are consistent with unidimensional, and to a lesser degree multidimensional solutions (i.e., items lacked a level of content diversity to substantiate a complex, reliable multifactor structure). DISCUSSION/CONCLUSION: Analytic results on the DIDS revealed reasonable psychometric properties as a measure of disability identity. Our results support using a sum or total score of disability identity. Results of this work are an important contribution to a growing body of literature supporting, and investigating, disability identity development. Furthermore, the DIDS measure with its resulting composite score of disability identity has the potential to inform clinicians in the field of rehabilitation psychology as well as informing future targeted interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".