Validity and Reliability of the Turkish Version of the Low Back Activity Confidence Scale (LoBACS)
Bibliographic record
Abstract
The Low Back Activity Confidence Scale (LoBACS) is a 15-item scale designed to assess low back pain (LBP) through self-efficacy, a key predictor of functional recovery. This study aimed to culturally adapt and evaluate the validity and reliability of the Turkish version of LoBACS in patients with LBP. The translation and adaptation followed Beaton et al.’s protocol. Content and face validity were assessed with a pre-patient group. Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to evaluate construct validity. Internal consistency, as well as test-retest reliability, were evaluated in a sample of 150 patients aged 18–70 years. Concurrent validity was measured alongside the Oswestry Back Pain Disability Questionnaire (ODQ) and Quebec Back Pain Disability Scale (QBPDS). Two factors emerged from factor analysis, with item loadings for Functional Self-efficacy (FnSE) ranging from 0.745 to 0.896 and for Self-Regulatory and Exercise Self-efficacy (Self-Reg&ExSE) from 0.817 to 0.940. Cronbach’s alpha was high for FnSE, Self-Reg&ExSE, and the total scale (α = 0.941). Total correlation for each item ranged between 0.770 and 0.925. Test-retest reliability was also high (r = 0.941, p < .01). LoBACS showed moderate agreement with ODQ and QBPDS, demonstrating concurrent validity. In conclusion, the Turkish version of LoBACS is a valid and reliable tool for measuring LBP-related self-efficacy.
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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 | 0.005 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".