Everyday emotion–goal pursuit associations in older adults are moderated by goal representations
Bibliographic record
Abstract
This study examines intra- and interindividual differences in everyday goal pursuit in older adults focusing on the role of emotions and goal representations. Assuming a prioritization of self-preservation in old age, we expected that reduced negative (and elevated positive) emotions would be associated with increased everyday goal pursuit. These links were expected to be moderated by goal representations such that positive emotions would be more strongly linked to greater goal pursuit when goals were represented as hopes, whereas negative emotions would be less strongly linked to reduced goal pursuit when goals were represented as fears. We used up to 21 surveys from 236 individuals collected over 7 days (Age: Mean = 70.5, 60-87 years). Multilevel models revealed that more intense positive emotional experiences and less intense negative emotional experiences were each associated with elevated everyday goal pursuit. As expected, hoped-for goals were associated with stronger positive emotion-goal pursuit associations. Feared goals were associated with weaker negative emotion (particularly worry)-goal pursuit links. Moderations were limited to the most salient goal. These findings provide insights into how everyday emotion dynamics and goal pursuit may be shaped by the way older adults represent their goals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".