Interpretation and content validity of the items of the numeric rating version short-WORC to evaluate outcomes in management of rotator cuff pathology: a cognitive interview approach
Bibliographic record
Abstract
BACKGROUND: The shortened version of the Western Ontario Rotator Cuff Index (Short-WORC) is a patient reported outcome measure that evaluates quality of life (QoL) of patients with rotator cuff pathology. However, formal content validation of the full or Short-WORC has not been reported. This study aims to understand how 1) people interpret and calibrate responses to items on the Short-WORC and 2) compensatory strategies that might enhance function and thereby affect responses. METHODS: This study uses cognitive interviewing, a qualitative methodology that focuses on the interpretation of questionnaire items. Patients with rotator cuff disorders (n = 10), clinicians (n = 6) and measurement researchers (n = 10) were interviewed using a talk aloud structured interview that evaluated each of the 7 items of the Short-WORC. All interviews were recorded and transcribed verbatim by one researcher (R.F). Analysis was done through an open coding scheme using a previously established framework. RESULTS: Overall, the items on the Short-WORC were well received by participants. Through the interviews, the 6 themes of: Comprehension, Inadequate response definition, Reference Point, Relevance, Perspective Modifiers and Calibration Across Items emerged. The items of working above the shoulder (90%), compensating with the unaffected arm (88%) and lifting heavy objects (92%) were the most relevant to participants. Participants calibrated their scores on the items of sleeping and styling (19%) the most. Perspective modifiers of gender, influenced the calibrations of items of styling your hair (30%) and dressing or undressing (19%). Compensatory strategies of task-re allocation and using assistive devices/resources were frequently mentioned by participants. Overall, participants had minor comprehension issues, but found the 7- items of the Short-WORC to be relevant to QoL. CONCLUSIONS: Therefore, the findings demonstrate that the Short-WORC is not cognitively complex, but varies with patient perspectives. Overall, the Short-WORC provides evidence of demonstrating strong content validity when used for rotator cuff disorder patients.
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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 | 0.076 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".