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Record W4229041100 · doi:10.36315/2022inpact057

EXAMINING ATTITUDES TOWARDS AGEING

2022· article· en· W4229041100 on OpenAlexaffabout
Madison Herrington, Lilly Both

Bibliographic record

VenuePsychological applications and trends · 2022
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGrandparentPsychologyDeath anxietyAnxietyGratitudePersonalityGerontologyMultilevel modelClinical psychologyContact hypothesisDevelopmental psychologySocial psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

"The purpose of this study was to identify predictors of ageism. Ageism occurs when demeaning attitudes are directed toward individuals in a certain age group. Several theories have been postulated as to why ageism towards older adults occurs, such as contact theory (i.e., the quantity and quality of contact with older adults), terror management theory (i.e., anxiety and fear of mortality), and modernization theory (i.e., a belief that the skills of older adults are obsolete). Research in this area has selectively tested different theories of ageism; however, these studies have failed to examine multiple theories within one model. The current study examined contact theory, terror management theory, and modernization theory with respect to ageism. We examined survey data from 291 undergraduate students at a small university in Atlantic Canada. The survey was conducted online. Demographic characteristics, contact with grandparents and non-related older adults, and quality of interactions were measured using self-generated questionnaires. In addition, measures of personality, gratitude, ageing anxiety, and fear of death were administered. Also, older adults’ knowledge, burden/contributions to family/society, and attitudes toward the elderly were measured. A hierarchical multiple linear regression analysis was conducted predicting ageist attitudes. The overall model was statistically significant and accounted for 63% of the variance. Both age and gender were found to be significant predictors; younger adults and men had higher scores on ageism. As well, participants who reported lower quality of contact with grandparents during childhood, and lower scores on their current quality of contact with older adults were more likely to endorse ageist attitudes. Of the five personality factors, lower scores on Agreeableness were a significant predictor. Finally, anxiety towards ageing (measuring terror management theory) and perceiving older adults as a burden (measuring modernization theory) predicted ageism. According to these findings, all ageism theories had an impact on ageist attitudes, but modernization theory contributed the most unique variance to the model. Overall, further research should continue to investigate the multidimensional construct of ageism."

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.078
GPT teacher head0.381
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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