Development, initial content validation and reliability of Nigerian Composite Lifestyle CVD risk factors questionnaire for adolescents
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease risk (CVD) factors affect every age category including adolescents in developing nations. Prevention strategies are effective only when there are epidemiological data for the targeted populations. The collection of such data is only made easy with composite lifestyle CVD risk factors measures that are culturally sensitive and acceptable among the target populations. OBJECTIVE: The objective of the study was to develop a culturally sensitive and friendly composite lifestyle CVD risk factors questionnaire for adolescents in Nigeria. METHODS: A systematic review was conducted to identify existing, published questionnaires from which items could be selected. Content and face validation were conducted using an expert panel and a sub-sample of the target population. Data was analyzed qualitatively and reliability was assessed using intra-class correlation and Kappa statistic. RESULTS: Based on the comments received from experts, the questions were restructured, simplified, clarified, formatted, some questions were added and expert reached a consensus. Kappa showed fair to moderate agreement in 65% of the questions and perfect agreement in one question. CONCLUSION: The CVD risk factors questionnaire has acceptable content validity and reliability and should be used to assess CVD risk factors among adolescents in Nigeria.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".