Cross-cultural adaptation, content validation, and reliability of the Nigerian Composite Lifestyle CVD Risk Factors Questionnaire for adolescents among Yoruba rural adolescents in Nigeria
Bibliographic record
Abstract
BACKGROUND: Assessment of lifestyle risk factors must be culturally- and contextually relevant and available in local languages. This paper reports on a study which aimed to cross culturally adapt a composite lifestyle cardiovascular disease (CVD) risk factors questionnaire into an African language (Yoruba) and testing some of its psychometric properties such as content validity and test retest reliability in comparison to the original English version. METHODS: This study utilized a cross sectional design. Translation of the English version of the questionnaire into Yoruba was undertaken using the guideline by Beaton et al. The translated instrument was presented to 21 rural adolescents to assess comprehensibility and clarity using a sample of convenience. A test retest reliability was conducted among 150 rural adolescents using a purposive sampling. Data was analyzed using intraclass correlation (ICC ) model 3, Cohen kappa statistics and prevalence rates. RESULTS: ICC ranged between 0.4-0.8. The Yoruba version was completed 15-20 minutes and was reported to be culturally appropriate and acceptable for rural Nigerian adolescents. CONCLUSIONS: The Yoruba translation of the Nigerian composite lifestyle risk factors questionnaire performs at least as well as the original English version in terms of content validity and reliability. It took a shorter time to complete therefore may be more relevant to rural adolescents.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".