Improving diet, self-rated health, & BMI for older adults with a student-led health education intervention
Bibliographic record
Abstract
Abstract Older adults in Alaska make up the fastest growing population in the state and face some of the greatest provider shortages in the country. Research suggests that older adults may respond better to positive, hopeful information that aligns with their desires and goals for the future. Therefore, we designed and delivered a 15-week, student-led health intervention using Persuasive Hope Theory to 39 older adults to increase feelings of hope and improve health. Our research question was: Did self-efficacy, fruit and vegetable intake, physical activity, self-rated health, or BMI improve significantly as a result of the program? Dependent sample t-tests revealed that the number of servings of fruit and vegetables consumed increased significantly after the program compared to before the program (p<.01). BMI was also statistically significantly lower after the program compared to before the program (p<.05) and participants’ self-rated health was significantly higher after the program compared to before the program (p<.01). There was no significant difference in self-efficacy or physical activity levels over time. A power analysis was conducted using G*Power version 3.1.9.7 that indicated the required sample size to achieve 80% power at a significance criterion of α = 0.5, is N = 27 for a dependent sample t-test. Despite the use of a small, convenience sample, results indicate that participants were satisfied with the student-led intervention that significantly helped to decrease BMI and increase fruit and vegetable intake and self-rated health among the sample population, which can serve as a model for other hope-based, intergenerational healthy aging initiatives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".