The English Knee Self‐Efficacy Scale is a valid and reliable measure for knee‐specific self‐efficacy in individuals with a sport‐related knee injury in the past 5 years
Bibliographic record
Abstract
PURPOSE: To translate and cross-culturally adapt the Swedish Knee Self-Efficacy Scale (K-SES) into English and evaluate the measurement properties in a sample of individuals with previous knee injury. METHODS: Translation, cross-cultural adaptation, and evaluation followed the Beaton multi-step process and COSMIN guidelines. Participants (n = 125) aged 16-60 years with a sport-related intra-articular tibiofemoral or patellofemoral injury within the last 5 years completed the K-SES, Knee Injury and Osteoarthritis Outcome Score, Anterior Cruciate Ligament-Return to Sport After Injury Scale, Tegner Activity Level Scale, and Multi-dimensional Health Locus of Control. Confirmatory factor analysis (CFA) tested a-priori two-factor structure and model fit. Cronbach-alpha, intra-class correlation coefficient (ICC), and absolute reliability (Bland-Altman plots) were calculated. Construct validity was assessed by eight pre-defined hypotheses. A sub-group of participants (n = 42) completed the K-SES twice to assess intra-rater reliability. RESULTS: The cross-cultural adaptation generated an English K-SES with face and content validity. The original two-factor structure was rejected based on CFA and a revised solution informed by Exploratory Factor analysis resulted in an adequate fit. All construct validity hypotheses were confirmed. The K-SES showed good internal consistency [Factor (F1: α = 0.96; F2: α = 0.73)], intra-rater reliability (ICC = 0.92), and no systematic bias between repeated measurements. CONCLUSION: The English K-SES is a valid and reliable measure for knee-specific self-efficacy in individuals who have sustained a sport-related intra-articular knee injury in the previous 5 years. LEVEL OF EVIDENCE: IV.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".