A systematic review and appraisal of methods of developing and validating lifestyle cardiovascular disease risk factors questionnaires
Bibliographic record
Abstract
BACKGROUND: Well developed and validated lifestyle cardiovascular disease (CVD) risk factors questionnaires is the key to obtaining accurate information to enable planning of CVD prevention program which is a necessity in developing countries. We conducted this review to assess methods and processes used for development and content validation of lifestyle CVD risk factors questionnaires and possibly develop an evidence based guideline for development and content validation of lifestyle CVD risk factors questionnaires. MATERIALS/METHODS: Relevant databases at the Stellenbosch University library were searched for studies conducted between 2008 and 2012, in English language and among humans. Using the following databases; pubmed, cinahl, psyc info and proquest. Search terms used were CVD risk factors, questionnaires, smoking, alcohol, physical activity and diet. RESULTS: Methods identified for development of lifestyle CVD risk factors were; review of literature either systematic or traditional, involvement of expert and /or target population using focus group discussion/interview, clinical experience of authors and deductive reasoning of authors. For validation, methods used were; the involvement of expert panel, the use of target population and factor analysis. CONCLUSION: Combination of methods produces questionnaires with good content validity and other psychometric properties which we consider good.
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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 | 0.065 | 0.207 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".