EXPLORING EMOTION CONTROL BELIEFS ACROSS THE LIFESPAN: ROLES OF AGE AND CHRONIC ILLNESS
Bibliographic record
Abstract
Abstract Existing theory and research highlights emotion control beliefs have important implications for emotion regulation and psychological well-being. Emotion control beliefs can be subdivided into four categories: the extent to which people believe 1) they can control their emotion (self-can beliefs), they should control their emotions (self-should beliefs), 3) others can control their emotions (others-can beliefs), and 4) other should control their emotions (others-should beliefs). To our knowledge, research has yet to examine how developmental factors are associated with emotion control beliefs in adults (i.e., age and non-normative experience of chronic illness). Past research highlights age related differences in emotional processes, and the influence of non-normative chronic illness on emotional well-being. Therefore, the present study utilized data from a lifespan sample of 47 participants (younger adults: n = 21, age range = 19-35, M = 27.55; older adults: n = 26, age range = 68-81, M = 72.40) to examine differences in emotion control beliefs among younger and older adults, people with and without chronic illness, and the interaction between these factors. Our analyses revealed levels of self-can beliefs were lower in people with chronic illness. In particular, younger adults with chronic illness reported the lowest levels of self-can beliefs. By contrast, among people without chronic illness, older adults reported lower levels of others-should beliefs, relative to younger adults. These findings inform theories of emotional aging, development, and health by suggesting emotion control beliefs vary with age and chronic illness.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".