Three facets of emotion regulation in old and very old age: Strategy use, effectiveness, and variability.
Bibliographic record
Abstract
= 1.46) were prompted 6 times a day for 7 consecutive days to report both their stressors and 10 emotion regulation strategies. Overall, there was little indication of age differences in the use of emotion regulation strategies during exposure to stressors, but very old, as compared with young old, individuals used three of the 10 strategies considered here more intensively. The 10 emotion regulation strategies were similarly effective across age groups based on their association with perceived overall emotion regulation success. We also did not find age group differences in within-strategy variability, defined as the variation in using a given strategy across stressor situations. By contrast, between-strategy variability, defined as the selective use of fewer rather than many strategies across stressor situations, was lower for very old participants. Only between-strategy, and not within-strategy, variability contributed to overall emotion regulation success. There was no age group difference in this regard. Taken together, the evidence suggests small age differences in emotion regulation if at all. This is noteworthy given the advanced age of the very old subsample in this study and the deficits in multiple domains of functioning reported in the literature for this advanced age. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".