Miami Prospective Memory Test in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to verify the effect of age, education and sex on Miami Prospective Memory Test (MPMT) performance obtained at baseline of the Canadian Longitudinal Study on Aging (CLSA) by neurologically healthy French- and English-speaking subsamples of participants (N = 18,511). METHOD: The CLSA is a nation-wide large epidemiological study with participants aged 45-85 years old at baseline. The MPMT is an event- and time-based measure of prospective memory, with scores of intention, accuracy and need for reminders, administered as part of the Comprehensive data collection. Participants who did not self-report any conditions that could impact cognition were selected, which resulted in 15,103 English- and 3408 French-speaking participants. The samples are stratified according to four levels of education and four age groups (45-54; 55-64; 65-74; 75+). RESULTS: There is a significant age effect for English- and French-speaking participants on the Event-based, Time-based, and Event- + Time-based scores of the MPMT. The effect of the education level was also demonstrated on the three MPMT scores in the English-speaking group. The score 'Intention to perform' was the most sensitive to the effect of age in both the English and French samples. Sex had no impact on performance on the MPMT. CONCLUSIONS: This study confirms the impact of age and level of education on this new prospective memory task. It informs future research with this measure including the development of normative data in French- and English-speaking Canadians on the Event-based and Time-based MPMT.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".