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Record W2790400048 · doi:10.1093/geront/gny011

Development and Psychometric Validation of a Questionnaire Assessing the Impact of Memory Changes in Older Adults

2018· article· en· W2790400048 on OpenAlexafffund
Komal T. Shaikh, Erica L. Tatham, Preeyam K. Parikh, Graham A. McCreath, Jill B. Rich, Angela K. Troyer

Bibliographic record

VenueThe Gerontologist · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest HospitalUniversity of TorontoYork University
FundersOntario Ministry of Health and Long-Term Care
KeywordsPsychologyPsychometric testingPsychometricsClinical psychologyGerontologyApplied psychologyMedicineInternal consistency

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Many healthy older adults experience age-related memory changes that can impact their day-to-day functioning. Qualitative interviews have been useful in gaining insight into the experience of older adults who are facing memory difficulties. To enhance this insight, there is a need for a reliable and valid measure that quantifies the impact of normal memory changes on daily living. The primary objective of this study was to develop and validate a new instrument, the Memory Impact Questionnaire (MIQ). RESEARCH DESIGN AND METHODS: We examined the underlying component structure and psychometric properties of the MIQ in a sample of 205 community-dwelling older adults. RESULTS: Principal component analysis revealed three clusters: (a) Lifestyle Restrictions, (b) Positive Coping, and (c) Negative Emotion. Comparisons of the corresponding subscale scores with scores on other instruments revealed good convergent and discriminant validity. In addition, the MIQ subscales and the total score showed good test-retest reliability (rs = 0.65-0.91) and internal consistency (αs = 0.87-0.93). DISCUSSION AND IMPLICATIONS: This novel questionnaire can be used in both clinical and research settings to better understand the impact of memory changes on the day-to-day functioning of older adults and to monitor outcomes of support programs for this population.

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 score0.238
Threshold uncertainty score0.135

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 studies0.0000.000
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 head0.047
GPT teacher head0.396
Teacher spread0.348 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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