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Record W2733730667 · doi:10.1093/geroni/igx004.3894

STEREOTYPE THREAT EFFECTS ON OLDER ADULTS’ MEMORY: A META-ANALYSIS

2017· article· en· W2733730667 on OpenAlexaff
Lu Yi Li, Khushboo S. Patel, Barbara Nachtrieb Armstrong, Sara N. Gallant, Bonny Yee-Man Wong

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStereotype threatPsychologyMeta-analysisStereotype (UML)Cognitive psychologySocial psychologyDevelopmental psychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Stereotype threat describes instances in which an individual is at risk of confirming negative stereotypes about a group they identify with. In line with this, research suggests that exposure to negative age-based stereotypes can undermine the cognitive performance of older adults. The objective of the current meta-analytic review was to determine the magnitude of age-based stereotype threat effects on older adults’ memory. Results revealed a significant and robust effect of stereotype threat induction on older adults’ memory performance across reviewed studies. Specifically, when exposed to negative aging stereotypes, older adults showed consistent decrements in memory relative to control groups. The moderating roles of various demographic and methodological characteristics on stereotype threat effects will be discussed. Overall, these findings have implications for the interpretation of memory performance on lab-based memory tests and how self-perceptions of aging can influence test results..

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.073
GPT teacher head0.397
Teacher spread0.324 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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