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Record W2480635747 · doi:10.1093/geronb/gbw083

Negative Aging Stereotypes Impair Performance on Brief Cognitive Tests Used to Screen for Predementia

2016· article· en· W2480635747 on OpenAlexaboutno aff
Marie Mazerolle, Isabelle Régner, Sarah J. Barber, Marc Paccalin, Aimé-Chris Miazola, Pascal Huguet, François Rigalleau

Bibliographic record

VenueThe Journals of Gerontology Series B · 2016
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsStereotype threatCognitionCognitive agingEffects of sleep deprivation on cognitive performanceMontreal Cognitive AssessmentPsychologyCognitive declineCognitive testGerontologyFalse positive paradoxCognitive skillStereotype (UML)Clinical psychologyDementiaCognitive impairmentDevelopmental psychologyMedicineSocial psychologyPsychiatryDisease

Abstract

fetched live from OpenAlex

OBJECTIVES: There is today ample evidence that negative aging stereotypes impair healthy older adults' performance on cognitive tasks. Here, we tested whether these stereotypes also decrease performance during the screening for predementia on short cognitive tests widely used in primary care. METHOD: An experiment was conducted on 80 healthy older adults taking the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) under Threat or Reduced-threat condition. RESULTS: Stereotype threat significantly impaired older adults' performance on both tests, resulting in 40% of older adults meeting the screening criteria for predementia, compared with 10% in Reduced-threat condition (MMSE and MoCA averaged). DISCUSSION: Our research highlights the influence of aging stereotypes on short cognitive tests used to screen for predementia. It is of critical importance that physicians provide a threat-free testing environment. Further research should clarify whether this socially induced bias may also operate in secondary care by generating false positives.

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 score0.525
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.124
GPT teacher head0.419
Teacher spread0.295 · 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

Citations50
Published2016
Admission routes1
Has abstractyes

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