Validated Measures of Illness Perception and Behavior in People with Knee Pain and Knee Osteoarthritis: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To identify validated measures that capture illness perception and behavior and have been used to assess people who have knee pain/osteoarthritis. METHODS: A scoping review was performed. Nine electronic databases were searched for records from inception through April 19, 2015. Search terms included illness perception, illness behavior, knee, pain, osteoarthritis, and their related terms. This review included English language publications of primary data on people with knee pain/osteoarthritis who were assessed with validated measures capturing any of 4 components of illness perception and behavior: monitor body, define and interpret symptoms, take remedial action, and utilize sources of help. Seventy-one publications included relevant measures. Two reviewers independently coded and analyzed each relevant measure within the 4 components. RESULTS: Sixteen measures were identified that capture components of illness perception and behavior in the target population. These measures were originally developed to capture constructs that include coping strategies/skills/styles, illness belief, illness perception, self-efficacy, and pain behavior. Coding results indicated that 5, 11, 12, and 5 of these measures included the monitor body, define and interpret symptoms, take remedial action, and utilize sources of help components, respectively. CONCLUSIONS: Several validated measures were interpreted as capturing some components, and only 1 measure was interpreted as capturing all of the components of illness perception and behavior in the target population. A measure that comprehensively captures illness perception and behavior could be valuable for informing and evaluating therapy for patients along a continuum of symptomatic knee osteoarthritis.
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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 | 0.023 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".