Preliminary Evaluation of the InCHARGE Program Among Older African Americans in Rural Alabama
Bibliographic record
Abstract
Objective: Blindness rates among older African Americans are two times higher than for older whites. Our purpose was to understand attitudes about eye care and perceived barriers to care among older African Americans living in rural Alabama and to determine whether an educational program reduced perceived barriers to care. InCHARGE, an eye health education program for older African Americans, promotes eye disease prevention by conveying the personal benefits of annual dilated comprehensive eye care and by teaching strategies to minimize barriers to eye care.\nDesign and Participants: InCHARGE was presented in five senior centers to 111 individuals. Using a questionnaire before and three months after InCHARGE, we evaluated what impact InCHARGE had on attitudes and knowledge about prevention and strategies for reducing barriers.\nResults: Before InCHARGE, 52.3% reported receiving an eye examination in the past year. Almost all indicated that they felt finding, getting to, and communicating with a doctor were not problems yet about one-quarter indicated that the cost of an examination and/or eyeglasses were problems. After InCHARGE the percentage saying that cost was a problem increased to almost half.\nConclusions: Older African Americans in rural Alabama have positive attitudes about comprehensive eye care, yet only about half reported receiving an exam by an eye care provider in the past year. The cost of care is a barrier for many, a problem that was not mitigated by InCHARGE. In order to improve eye health in this population, eye health education initiatives are not enough; economic strategies must be implemented to address the cost barrier.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".