Exploring Website Preferences for African American Women: An Evaluation of an Internet-Based Source of Health Information on Eating Healthy and Being Active
Bibliographic record
Abstract
INTRODUCTION: Internet-based health interventions continue to be popular and effective, and one area of focus of such interventions is weight loss. Although African-American women are regular users of Internet-based health interventions, there is a dearth of research regarding Internet usage and website preferences of this group. The purpose of this study was to explore the relationship between website attributes that influence African American women to use health-related websites, their stage of change for using the Internet to access information on health care, and predictor variables for website ratings. METHODS: The study used a backwards stepwise regression analysis to determine the best predictor of high ratings of the Eat Healthy - Be Active web portal and the Rating and Evaluating Health Care Websites Survey to measure website attitudes and beliefs and stage of change for using the computer and Internet to access health care information. The participants were 206 African American women who use the Internet. RESULTS: The regression analysis indicated that the predictor variables were education level, BMI, and weight. CONCLUSION: This study demonstrates that various factors influence the effectiveness of Internet-based interventions targeted at African-American women. Future research should continue to explore these factors, particularly for groups with higher rates of preventable diseases.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".