Successful Aging Among African American Older Adults With Type 2 Diabetes: Table 1.
Bibliographic record
Abstract
OBJECTIVES: Rowe and Kahn's concept of successful aging remains an important model of well-being; additional research is needed, however, to identify how economically and socially disadvantaged older adults experience well-being, including the role of life events. The findings presented here help address this gap by examining the subjective construction of well-being among urban African American adults (age ≥ 50) with Type 2 diabetes. METHOD: As part of the National Institute on Aging-funded Subjective Experience of Diabetes among Urban Older Adults study, ethnographers interviewed African American older adults with diabetes (n = 41) using an adaptation of the McGill Illness Narrative Interview. Data were coded using an inductively derived codebook. Codes related to aging, disease prognosis, and "worldview" were thematically analyzed to identify constructions of well-being. RESULTS: Participants evaluate their well-being through comparisons to the past and to the illnesses of friends and family. Diabetes self-care motivates social engagement and care of others. At times, distrust of medical institutions means well-being also is established through nonadherence to suggested biomedical treatment. DISCUSSION: Hardship and illness in participants' lives frame their diabetes experience and notions of well-being. Providers need to be aware of the social, economic, and political lenses shaping diabetes self-management and subjective well-being.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".