A Meta-Analysis of Positive and Negative Age Stereotype Priming Effects on Behavior Among Older Adults
Bibliographic record
Abstract
OBJECTIVE: Evidence has shown that age stereotypes influence several behavioral outcomes in later life via stereotype valence-outcome assimilation; however, a direct comparison of positive versus negative age stereotyping effects has not yet been made. METHODS: PsycINFO and Pubmed were used to generate a list of articles (n = 137), of which seven were applicable. From these articles, means, standard errors (SEs), and other relevant data were extracted for 52 dependent measures: 27 involved negative age primes and 25 involved positive age primes. Independent samples analysis of variance tests were used to explore the influence of prime valence and awareness on behavior compared with a neutral referent. RESULTS: A significant main effect for prime valence was found such that negative age priming elicited a greater effect on behavior than did positive age priming (F(1,48) = 4.32, p = .04). In fact, the effects from negative age priming were almost three times larger than those of positive priming when compared with a neutral baseline. This effect was not influenced by prime awareness, discipline of study, study design, or research group. DISCUSSION: Findings show that negative age stereotyping has a much stronger influence on important behavioral outcomes among older adults than does positive age stereotyping.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 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".