Dimensions of stereotypical attitudes among older adults: Analysis of two countries
Bibliographic record
Abstract
AIM: Much research on attitudes towards older adults has used younger adults as participants and identified a range of negative attitudes towards older persons. Comparatively little literature has explored the attitudes of older adults themselves towards their own age cohort. METHODS: The present study explicitly compared attitudes towards other older adults from samples of 195 older adults in Australia and 172 older Canadians. Attitudinal measures included the Aging Attitudes Questionnaire (assesses older adults' attitudes toward other older adults), Fraboni Scale of Ageism (assesses younger adults' attitudes toward older adults) and the Reactions to Aging Questionnaire (assesses attitudes toward one's own aging), as well as a scale measuring knowledge of aging, the Facts on Aging Quiz, adapted for Australia and Canada. Responses on the three attitudinal measures were subjected to principal components analysis. RESULTS: Two components emerged in both samples, one defined by the Reactions to Aging Questionnaire and Aging Attitudes Questionnaire scales and the second by the Fraboni Scale of Ageism scales. Regression analyses to ascertain prediction of scores on the Facts on Aging Quiz, adapted for Australia and Facts on Aging Quiz, adapted for Canada showed that only the Aging Attitudes Questionnaire scale for Physical Changes predicted scores on the Facts on Aging Quiz, adapted for Australia and no attitudes predicted Facts on Aging Quiz, adapted for Canada scores. CONCLUSIONS: It appears that older adults distinguish between their own aging and aging in others. Knowledge of aging appears to be predicted only by attitudes toward physical changes. Given increasing proportions of older adults in the population, as well as increasing access to aging information available to older cohorts, continued research on how older adults view themselves and the aging process is important, and will almost certainly continue to evolve over time. Geriatr Gerontol Int 2016; 16: 1226-1230.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".