Factors associated with attitudes toward the elderly in a sample of elderly caregivers
Bibliographic record
Abstract
BACKGROUND: The number of elderly caregivers is increasing in the world. It is important to know the attitudes toward the elderly, because they can influence a range of behaviors. Our aim was to determine factors associated with attitudes toward the elderly in a sample of older carers. METHODS: Three hundred and thirteen elderly caregivers (75.4% women, mean age 69.7 ± 7.1) who cared for a dependent older person at home completed a cross-sectional household interview. In addition to the four domains of the Neri Scale to Assess Attitudes Toward the Elderly, participants were evaluated regarding the demographics, care recipient (CR) characteristics, functional and cognitive status, general health, life satisfaction, perceived stress, and depressive symptoms. RESULTS: Overall, attitudes toward the elderly were neutral in this sample. More negative attitudes in some Neri Scale domains were associated with being older, living in an urban setting, taking more medications per day, caring for an elderly dependent in basic Activities of Daily Living (ADLs), being "more or less" satisfied with life, and having higher levels of perceived stress. There was a negative association between positive attitudes and educational level. CONCLUSIONS: The results highlight the need for public policies to promote more positive attitudes toward aging and change negative stereotypes usually used to designate older people. These public policies can try to modify some predictors of negative attitudes, such as perceived stress, which was associated with all four domains of Neri Scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".