Evaluating the validity of the Amharic Brief Pain Inventory among people with chronic primary musculoskeletal pain in Ethiopia
Bibliographic record
Abstract
BACKGROUND: The Brief Pain Inventory (BPI) is a multidimensional pain assessment tool used to evaluate pain severity and pain interference. The BPI has been translated and validity estimated across multiple languages and patient populations for clinical and research settings. This study aimed to assess the reliability and validity of Amharic BPI test scores among patients with chronic primary musculoskeletal pain living in Ethiopia. METHODS: This study had two parts: cognitive interviews and psychometric testing. An expert committee reviewed the Amharic BPI, and fifteen participants participated in the cognitive interviews. The results from the cognitive interviews were evaluated, and the committee approved recommendations for the tool prior to psychometric testing. Two hundred and sixty-nine patients were recruited from three sites for the psychometric testing. The results were summarised using descriptive statistics. Cronbach's alpha was calculated to estimate the internal consistency. To assess test-retest reliability, the intraclass coefficient was examined, and a Bland-Altman plot was created. Construct validity was determined using confirmatory factor analysis by testing BPI's previously suggested two or three-factor dimensionalities. Convergent validity was assessed by estimating the correlation between the Amharic BPI and SF-36 subscales. RESULTS: The Amharic BPI scores showed a good internal consistency using a 2-factor model with α = 0.89 for pain severity and α = 0.91 for pain interference. Good internal consistency was also observed in the 3-factor model, with α = 0.89 for pain severity, α = 0.84 for activity interference, and α = 0.86 for affective interference items. The test-retest reliability testing resulted in an ICC = 0.82 for pain severity and ICC = 0.90 for the pain interference. The severity scale had the highest correlation with bodily pain subscale of the SF-36 at r = - 0.44, and the interference scale with Physical functioning scale of SF-36 at r = - 0.63. Confirmatory factor analysis support rating Amharic BPI using a two-factor approach. CONCLUSIONS: Our findings showed that Amharic BPI scores demonstrate internal consistency, test-retest reliability, and construct validity among patients with chronic primary musculoskeletal pain in Ethiopia. Accordingly, the tool can be used in clinical practice or research in similar settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".