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Record W2119259098 · doi:10.1080/03601270903534796

Measures for Assessing Student Attitudes Toward Older People

2010· article· en· W2119259098 on OpenAlexaboutno aff
Xiaoping Lin, Christina Bryant, Jennifer Boldero

Bibliographic record

VenueEducational Gerontology · 2010
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsImplicit-association testImplicit attitudeScale (ratio)PsychologyOlder peopleTest (biology)Measure (data warehouse)Social desirabilitySocial psychologyGerontologyMedicineComputer science

Abstract

fetched live from OpenAlex

Measuring medical and allied health students' attitudes towards older people has been identified as an important research area. The present study compared the use of implicit and explicit attitude measures. Sixty-five undergraduates completed one explicit measure, the Fraboni Scale of Ageism (FSA), (Fraboni, Saltstone, & Hughes, 1990 Fraboni , M. , Saltstone , R. , & Hughes , S. ( 1990 ). The Fraboni Scale of Ageism (FSA): An attempt at a more precise measure of ageism . Canadian Journal on Aging , 9 , 56 – 66 .[Crossref], [Web of Science ®] , [Google Scholar]) and one implicit measure, the Implicit Association Test. They had positive explicit and neutral implicit attitudes towards older people, suggesting more positive attitudes than previously reported. These attitudes were related but there were discrepancies. Because explicit measures are likely to be influenced by factors such as social desirability, the implicit measure might be a more reliable measure.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.111
GPT teacher head0.493
Teacher spread0.382 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2010
Admission routes1
Has abstractyes

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