The relationship between depressive symptoms, metamemory, and prospective memory in older adults
Bibliographic record
Abstract
INTRODUCTION: Depression has been associated with impairments in different cognitive domains in younger adults, including prospective memory (PM; the ability to plan and execute intended actions in the future). However, it is still not well documented nor understood whether depression is also associated with impaired PM in older adults. The current study aimed to examine the association between depressive symptoms and PM in young-old and old-old adults, and to understand the potential influence of underlying factors, such as age, education, and metamemory representations (one's belief about their memory abilities). METHOD: = 6.09; range = 70-98 years). RESULTS: Bayesian ANCOVA revealed a 3-way interaction between depressive symptoms, age, and metamemory representations, indicating that the association between depressive symptoms and PM performance depended on age and metamemory representations. In the lower depressive symptoms group, old-old adults with higher metamemory representations performed as well as young-old adults independently of their metamemory representations. However, in the higher depressive symptoms group, old-old adults with higher metamemory representations performed more poorly than young-old adults with higher metamemory representations. CONCLUSION: This study indicates that metamemory representations may buffer the negative effect of age on PM performance only in old-old individuals with low depressive symptoms. Importantly, this result provides new insight into the mechanisms underlying the association between depressive symptoms and PM performance in older adults as well as into potential interventions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".