Translation of the Fear Avoidance Beliefs Questionnaire Into Hausa Language
Bibliographic record
Abstract
BACKGROUND: Self-report measures of fear-avoidance beliefs are widely used in clinical practice and research. To date there is no Hausa version of the Fear Avoidance Beliefs Questionnaire (FABQ). This is important as the Hausa language is a widely spoken language in West Africa. OBJECTIVES: The purpose of this study was to translate and validate the Hausa version of the FABQ in patients with non-specific neck pain. METHODS: Two independent bilingual Hausa translators translated the English version of the FABQ into Hausa which was thereafter back translated by one independent bilingual translator. A professional expert panel revised the translations to produce a consensus version. The psychometric testing of the final translated instrument was investigated by surveying 54 Hausa speaking patients with chronic non-specific neck pain. Cross-sectional construct validity was evaluated by comparing Hausa Fear Avoidance Beliefs Questionnaire (FABQ-H) with the English version of the FABQ. Internal consistency of the FABQ-H was examined by Cronbach alpha by comparing the scores between the FABQ-H and its subscales. Test-retest reliability was evaluated by administering the Hausa version twice. RESULTS: The translated Hausa version of FABQ proved to be acceptable. The FABQ-H showed strong correlations (r=0.94, p=0.000) with the original English version. There was also high internal consistency between the FABQ-H and its subscales (physical activity component-alpha=0.88, p=0.000 and work component- alpha=0.94, p= 0.000). The FABQ-H also showed a high test-retest reliability (intra-class correlation coefficient =0.98). CONCLUSION: The FABQ-H demonstrated excellent psychometric properties similar to other existing versions. The FABQ-H is recommended for clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".