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Record W2098310155 · doi:10.1177/1073191110370116

The Dutch Memory Compensation Questionnaire

2010· article· en· W2098310155 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAssessment · 2010
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsUniversity of Alberta
FundersNational Institute on Aging
KeywordsPsychologyCompensation (psychology)Cognitive psychologyClinical psychologyApplied psychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

The Memory Compensation Questionnaire (MCQ) is a psychometrically sound instrument that assesses the variety and extent to which an individual compensates for actual or perceived memory losses. Until now, only an English version of the MCQ has been psychometrically evaluated. The aim of the present study was to establish a Dutch version of the MCQ and evaluate its psychometric properties. The MCQ data of N = 556 cognitively healthy adults (61.8% females) aged between 50.1 and 95.3 years (M = 73.9 years, SD = 8.0) were analyzed. The results showed that the factor structure of the Dutch version of the MCQ corresponded well with that of the English version of the MCQ. The reliabilities of the scales of the Dutch version of the MCQ were all high (all Cronbach's αs ≥ .77). Demographic variables (especially age and gender) affected most of the MCQ scale scores. Regression-based normative data that take these demographic influences into account were established, and a user-friendly computer program was provided to facilitate the scoring and norming of the MCQ.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.354
Teacher spread0.339 · 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