INDIVIDUAL AND RELATION-INFERRED SELF-EFFICACY FOR PHYSICAL ACTIVITY INCREASE IN OLDER ADULT COUPLES
Bibliographic record
Abstract
Abstract Older adults encounter daily problems which may hinder them to engage in physical activity. Individual self-efficacy has been positively associated with physical activity, but it does not consider the social world we live in; relation-inferred self-efficacy does, as it captures one’s perception about whether a close other believes in their abilities to engage in these behaviors. With a sample of 110 community-dwelling older adult couples who wore accelerometers and reported daily ratings of problems for up to 7 days, this project analyzes associations of both types of self-efficacy and daily problems with physical activity (MVPA and steps). Assuming that both modalities of self-efficacy may be particularly useful to motivate individuals, interactions of self-efficacy and daily problems were examined. We controlled for well-established associations with physical activity (age, gender, and physical limitations). Daily problems were not associated with physical activity. When modelled separately, both types of self-efficacy were associated with more physical activity; when modelled together, only relation-inferred self-efficacy was significant. Unfortunately, there were no significant interaction effects. Findings replicate previous evidence and shed light on the importance of social context for physical activity engagement in older adulthood.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".