STRENGTH OF ATTACHMENT TO RELIGIOUS BELIEFS DECREASES NEGATIVE ATTITUDES TOWARD OLDER ADULTS
Bibliographic record
Abstract
Abstract Ageism, a term identified by Butler in the 1960s, refers to any discrimination towards an individual based on their age: implicit, explicit, or internalized. The impacts of experiencing ageism are far reaching, including negative impacts on memory, mood, physical health, and even mortality. Two recent studies (Neto et al., 2023; Kim & Jung, 2020) investigated the protective role religiosity may have on attitudes towards older adults. These studies provided preliminary evidence that groups or cultures with stronger religious beliefs had better attitudes towards older adults. The current study examined the impact of attachment to a belief system and death anxiety on ageism in a population of adults (21-73 years old, N=31). Participants reported a variety of belief systems, including Christian, Buddhist, Muslim, and Atheist/Agnostic, and were separated into two groups based on the strength of their beliefs (attached or unattached). ANCOVA analyses showed that while holding death anxiety constant, there was no impact of religious attachment on positive ageism (F(1,14)=0.98, p=0.34, partial eta2=0.06), there was an impact of religious attachment on negative ageism (F(1,14)=4.52, p=0.05, partial eta2=.24), and belief that lives of older adults should be restricted ((F(1,14)=5.24, p=0.045, partial eta2=.34), both with large effect sizes. Although the sample was small and likely underpowered, the effect sizes suggest more research in this area is warranted. The sample was diverse in terms of belief systems and the results are discussed in the context of strength of belief system, including atheism or agnosticism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".