Prospective Memory Is a Key Predictor of Functional Independence in Older Adults
Bibliographic record
Abstract
OBJECTIVES: Prospective memory (PM), the ability to execute delayed intentions, has received increasing attention in neuropsychology and gerontology. Most of this research is motivated by the claim that PM is critical for maintaining functional independence; yet, there is a dearth of empirical evidence to back up the claims. Thus, the present study tested whether PM predicts functional independence in older adults using validated behavioral performance measures for both PM and functional independence. METHODS: Fifty-eight healthy older adults performed a computerized PM paradigm, the Virtual Week task, as well as a timed version of an instrumental activities of daily living (TIADL) task. Furthermore, we assessed vocabulary, processing speed, and self-reported prospective remembering. RESULTS: TIADL scores correlated significantly with performance in the Virtual Week task, vocabulary, and processing speed. Hierarchical linear regressions revealed that vocabulary and Virtual Week performance were significant predictors for TIADL. However, self-reported PM scores did not predict everyday functioning. CONCLUSIONS: The findings indicate that PM is an important cognitive ability for successful and independent everyday life beyond vocabulary. Moreover, the results show a substantial incremental contribution of intact PM performance for the prediction of everyday functioning by using objective PM measures. (JINS, 2018, 24, 640-645).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".