THE RELATIONSHIP OF AGEISM, INTENTION TO WORK WITH OLDER ADULTS, AND SOCIAL DESIRABILITY
Bibliographic record
Abstract
Abstract Previous research demonstrates: 1) men and younger adults have higher negative ageism scores than women and older adults 2) higher scores of negative ageism are associated with lower intention to work with older adults and 3) women and older adults have higher scores for social desirability. It remains unclear how these factors interact. University students (N=547) aged 16 - 59 (Mean = 20.6) completed a survey measuring positive and negative attitudes towards older adults, intention to work with older adults, and social desirability. ANOVAs found a significant effects in negative ageism based on age, F(1, 3) = 6.69, p = 0.01, ω2 = 0.01, and gender, F(1, 3) = 11.43, p = 0.001, ω2 = 0.02, with a small effect size, but no significant interaction between age and gender. Young adults (M = 22.3) and males (M = 21.5) demonstrated more negative ageism than middle aged adults (M = 23.1) and females (M = 22.7) (lower scores indicate negative attitudes). An ANOVA of gender x age x social desirability was also significant for negative ageism, F(11) = 2.00, p = 0.03. However, there were no significant effects or interactions for gender or age on positive ageism and intention to work with older adults, or when social desirability was added. Although there were differences between demographic and social desirability groups for negative ageism, this relationship was not found for positive ageism. We expected social desirability to play a role in ageism, but this was not the case in the current sample.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".