Challenging and Dismantling Ageist Attitudes, Beliefs, and Behaviors Through Intergenerational Programs
Bibliographic record
Abstract
Abstract Age stereotypes are complex and multifaceted: individuals can demonstrate and embody numerous and varied positive and negative stereotypes. Therefore, solutions to combat age stereotypes must also be complex and multifaceted. Additionally, both social and physical forms of age segregation are common in our society. This causes fewer and fewer opportunities for younger and older people to interact. Intergroup Contact Theory suggests age stereotypes can be reduced through increased intergenerational contact. One way to encourage contact between younger and older populations is through intergenerational programming. However, there is a lack of literature investigating the effects of intergenerational programs on perceptions of aging. The purpose of this paper was to critically review and explore literature on intergenerational programs to understand how they influence age stereotypes and ageist attitudes. The available literature suggests that intergenerational programs involving young children (ages 4-8), adolescents (ages 11-18), or emerging adults (ages 19-26) interacting with older adults (ages 65+) can significantly reduce age stereotypes towards older adults. Additionally, older adults (ages 65+) negative beliefs and attitudes towards younger people (ages 4-26) can also be deconstructed after participation in intergenerational programs. Intergenerational programs act to break down age barriers and promote connections and understandings between generations. These programs challenge the belief that older and younger people should live and participate in spaces that are separate from one another. Providing opportunities for younger and older people to participate in intergenerational programs is one way to promote respectful relationships and enhance the quality of life and health of all generations.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".