Long-term Stability and Variability in Memory Compensation among Older Adults: Evidence from the Victoria Longitudinal Study
Bibliographic record
Abstract
摘要： 两种记忆状况组来自于参与加拿大维多利亚纵向研究(VLS)的老年人原样本。一种是未受损伤的控制组(NIC), 另一种是轻度记忆损伤组(MMD)。在基线水平和纵向水平上(即5次追踪或12年)比较了他们在日常生活中对记忆补偿技术的使用。我们使用多水平模型(以年龄和教育水平为协变量)来检验基线水平的差异和长期的变化模式。基线的结果表明MMD组被试报告了近期在日常生活中使用记忆补偿策略方面有更大的增长。纵向的结果表明记忆补偿的使用在12年中具有明显的稳定性, 但是与努力相关的补偿机制存在显著组间差异。教育水平这一协变量(可认为是潜在的认知储备指标)和三种记忆补偿策略随时间的不同变化相关。
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 |
| 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 teacher head, 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".