Confirmatory Factor Analysis of the Michigan Hand Questionnaire
Bibliographic record
Abstract
BACKGROUND: When the Michigan Hand Questionnaire (MHQ) was originally developed, an exploratory factor analysis (EFA) was used to reduce the originally large number of generated items to the 63 items currently present on the questionnaire. Confirmation of the implied factor model of the existing MHQ has never been performed. The objective of this study was to confirm the factor model used to create the existing MHQ, and to possibly shorten the existing MHQ using factor analysis. METHODS: Patients attending the Plastic Surgery Clinic at the QEII Health Sciences Centre with a hand complaint were asked to complete the MHQ. Confirmatory factor analysis was performed to explore the implied factor structure of the original EFA and to examine the interplay between the MHQ subscales. Further item-reduction was performed using clinically guided decisions as well as factor analysis-guided statistics. RESULTS: Initial confirmatory factor analysis showed that original EFA model does not optimally explain the relationships between items in the existing MHQ and their corresponding factors. Our abbreviated model of the MHQ consists of 23 items, and performed more favorably in all goodness-of-fit parameters than the original 63-item questionnaire. CONCLUSIONS: The factor model of the existing MHQ does not fully take advantage of the relationship between items in the MHQ and the proposed factors. This study proposes a shortened version of the MHQ that more accurately reflects hand health as well as a factor-based interpretation of the subscales that takes interdependent relationships into account.
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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 | 0.027 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".