Development and content validity of the musculoskeletal self-management questionnaire (MSK-SMQ)
Bibliographic record
Abstract
BACKGROUND: Self-management is recommended for managing persistent musculoskeletal conditions. In self-management, standardized and validated measurements (e.g., questionnaires) should be used. However, there is no general questionnaire to evaluate the level of self-management in people with persistent musculoskeletal conditions. OBJECTIVES: To develop a generic questionnaire to evaluate the level of self-management and self-management skills in people with persistent musculoskeletal conditions. DESIGN: Measurement properties study focused on the development and content validity of the Musculoskeletal Self-Management Questionnaire (MSK-SMQ). METHODS: The MSK-SMQ was developed, consisting of 24 questions. To assess the content validity of the MSK-SMQ, three panels (patients, professionals, researchers/academics) were used. The relevance, clarity and essentiality of each question was evaluated. Moreover, specific feedback could be provided. The Content Validity Index (CVI) was used to test content validity (Item-CV [I-CVI]) and the Scale-level-CVI [S-CVI]). The CVI was calculated for both relevance and clarity. The essentiality of each item was measured with the content validity ratio (CVR). RESULTS/FINDINGS: 91 people participated in this study. The overall content validity (relevance) was excellent, with an S-CVI of 0.96. Overall clarity was also excellent, with a score of 0.97. The range of the I-CVI for relevance was 0.91-1.00 and the range for clarity was 0.93-1.00. The mean CVR value was 0.51 and ranged from 0.14 to 0.87. CONCLUSIONS: The content validity of the questionnaire was found to be excellent. The study resulted in a revised version of the MSK-SMQ, which can be used in future research to determine further psychometric properties.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".