The measurement of participant-reported memory across diverse populations and settings: a systematic review and meta-analysis of the Multifactorial Memory Questionnaire
Bibliographic record
Abstract
The Multifactorial Memory Questionnaire (MMQ) is a participant-reported measure of memory satisfaction, ability, and strategy use. Initially validated with healthy older adults, it has since been used in many different populations and settings for a variety of purposes. We conducted a systematic review and meta-analysis of the measurement properties of the MMQ across multiple, diverse studies. METHODS: The study was designed using the Consensus-Based Standards for the Selection of Health Measurement Instruments and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We calculated aggregate statistics and evaluated the methodological quality of 29 studies retrieved from PsycINFO, MEDLINE, EMBASE, and Web of Science. RESULTS: Analyses revealed high-quality evidence for internal consistency, stability, measurement error, convergent validity, and known-groups validity of the three MMQ scales. There was moderate-quality evidence for responsiveness and structural validity, with some studies identifying separate factors for internal and external memory strategy use. Measurement properties were similar across languages, participant samples, and study designs. CONCLUSIONS: The MMQ is a valid, reliable, and responsive measure across diverse settings and populations. Future research is needed to determine whether more detailed information can be obtained from the scales, specifically, internal versus external strategy use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".