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Record W1983171849 · doi:10.1080/13607860410001649581

Measuring awareness of financial skills: reliability and validity of a new measure

2004· article· en· W1983171849 on OpenAlexafffund
Kenneth M. Cramer, Holly Tuokko, Catherine A. Mateer, David F. Hultsch

Bibliographic record

VenueAging & Mental Health · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of Victoria
FundersAlzheimer SocietySpencer Foundation
KeywordsPsychologyDiscriminant validityConvergent validityClinical psychologyNeuroticismCognitionReliability (semiconductor)Cognitive skillPsychometricsDementiaInternal consistencyPsychiatrySocial psychologyMedicinePersonality

Abstract

fetched live from OpenAlex

This paper examines the psychometric properties of a three-part (participant, informant, and performance) Measure for assessing Awareness of Financial Skills (MAFS). The MAFS was administered to 10 seniors with dementia and 25 well-functioning seniors, and their informants. Measures of cognitive functioning, social desirability, neuroticism, and perceived control were administered to each participant to allow for an assessment of validity. Internal consistency estimates for the participant and informant questionnaires were found to be 0.92 and 0.97, respectively. Convergent validity analysis indicated that performance on this measure was related to level of cognitive functioning, with higher level of unawareness associated with decreased cognitive ability. Discriminant validity analysis showed that performance on this measure was not related to social desirability or neuroticism. This study provides evidence that the MAFS is a reliable and valid tool for assessing awareness of financial skills in older adults.

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)0.006
metaresearch head score (Gemma)0.024
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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.275
Teacher spread0.239 · 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

Citations46
Published2004
Admission routes2
Has abstractyes

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