Validity evidence for the back beliefs questionnaire in the general population
Bibliographic record
Abstract
BACKGROUND: Despite common usage of the back beliefs questionnaire (BBQ) in a variety of studies, important validity evidence is missing. The objective of this study was to examine the validity of the BBQ in the general population. METHODS: A population-based, cross-sectional study design was used. Adult residents in two Canadian provinces were randomly sampled. To examine structural validity, items from the BBQ were subjected to factor analysis. Construct validity was tested by examining two hypotheses: BBQ scores would be most pessimistic in those with a recent history of back pain and in those who utilized passive treatments for back pain. Multiple linear regression was used to analyse the two hypotheses. RESULTS: Complete data were available for 6171 subjects. The best structure for the BBQ was to use eight or nine of the 14 items for scoring, which is consistent with the structure reported by the BBQ developers. BBQ scores varied based on participants' history of low back pain (LBP) and depended on pain severity. Those with severe pain and a recent history of LBP had the most pessimistic BBQ scores. In addition, participants who utilized passive management behaviours such as bed rest and activity avoidance had more pessimistic BBQ scores compared with those that did not. CONCLUSIONS: This study provides strong validity evidence supporting the current structure and scoring of the BBQ. In addition, construct validity was evidenced by the behaviour of BBQ scores in a manner congruent with our hypotheses, further supporting use of the BBQ in the general population.
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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 | 0.056 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".