Development and validation of a shoulder-specific body-perception questionnaire in people with persistent shoulder pain
Bibliographic record
Abstract
BACKGROUND: There is evidence that people with persistent shoulder pain exhibit findings consistent with the presence of sensorimotor dysfunction. Sensorimotor impairments can manifest in a variety of ways, and further developing our understanding of sensorimotor dysfunction in shoulder pain may improve current models of care. The Fremantle Back Awareness Questionnaire (FreBAQ) has been developed to assess disturbed body perception specific to the back. The purpose of the present study was to develop a shoulder-specific self-perception questionnaire and evaluate the questionnaire in people with persistent shoulder pain. METHODS: The Fremantle Shoulder Awareness Questionnaire (FreSHAQ-J) was developed by modifying the FreBAQ. One hundred and twelve consecutive people with persistent shoulder pain completed the FreSHAQ-J. Thirty participants completed the FreSHAQ-J again two-weeks later to assess test-retest reliability. Rasch analysis was used to assess the psychometric properties of the FreSHAQ-J. Associations between FreSHAQ-J total score and clinical status was explored using correlational analysis. RESULTS: The FreSHAQ-J has acceptable category order, unidimensionality, no misfitting items, and excellent test-retest reliability. The FreSHAQ-J was moderately correlated with disability and pain catastrophization. CONCLUSIONS: The FreSHAQ-J fits the Rasch measurement model well and is suitable for use with people with shoulder pain. Given the relationship between the FreSHAQ-J score and clinical status, change in body perception may be worth assessing when managing patients with shoulder pain.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".