Attitudes Toward Aging among Widowed Women: Identifying Profiles and Associated Factors
Bibliographic record
Abstract
Abstract Although previous research reports that widowed middle-aged and older adults’ attitudes toward aging are closely linked to their psychological well-being and health status, their distinct patterns of attitudes toward aging remain understudied. This study aims to identify patterns of attitudes toward aging among widowed middle-aged and older women and examine factors associated with these profiles. Using a nationally representative sample drawn from the 2020 and 2022 Health and Retirement Study (HRS), this study analyzes 1,223 widowed women, aged 48 to 99. The dataset includes responses to the Leave-Behind Questionnaire, which was distributed to half of the participants in 2020 and the other half in 2022. Attitudes toward aging were assessed using eight items on a six-point scale. Results from Latent Profile Analysis identified three attitudinal profiles based on attitudes toward aging, with higher scores indicating a more positive perception of aging: Low (36.7%), Middle (30.7%), and High (32.5%). Results from multinomial logistic regression showed that, compared to the Low group, older age (OR = 0.95, p<.001) and higher depressive symptoms (OR = 0.75, p<.001) were associated with lower odds of belonging to the High group. In contrast, those with better self-rated health (OR = 1.57, p<.001) were more likely to be in the High group. These findings highlight the role of self-rated health in fostering positive attitudes toward aging, while older age and depressive symptoms may be linked to more negative perceptions. These results suggest opportunities for interventions to support widowed women develop a more positive perspective on aging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".