The unidimensionality of the five Brain Injury Rehabilitation Trust Personality Questionnaires (BIRT-PQs) may be improved: preliminary evidence from classical psychometrics
Bibliographic record
Abstract
Objective: To assess the internal construct validity (ICV) of the five Brain Injury Rehabilitation Trust Personality Questionnaires (BIRT-PQ) with Classical Test Theory methods.Methods: Multicenter cross-sectional study involving 11 Italian rehabilitation centers. BIRT-PQs were administered to patients with severe Acquired Brain Injury and their respective caregivers. ICV was assessed by the mean of an internal consistency analysis (ICA) and a Confirmatory Factor Analysis (CFA).Results: Data from 154 patients and their respective caregivers were pooled, giving a total sample of 308 subjects. Despite good overall values (alphas ranging from 0.811 to 0.937), the ICA revealed that several items within each scale did not contribute as expected to the total score. This result was confirmed by the CFA, which showed the misfit of the data to a unidimensional model (RMSEA ranging from 0.077 to 0.097). However, after accounting for local dependency found within the data, fitness to a unidimensional model improved significantly (RMSEA ranging from 0.050 to 0.062).Conclusion: Despite some limitations, our analyses demonstrated the lack of ICV for the BIRT-PQ total scores. It is envisaged that a more comprehensive ICV analysis will be performed with Rasch analysis, aiming to improve both the measurement properties and the administrative burden of each BIRT-PQ.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".