A - 16Individual Differences in Affective Traits Role in Physical and Cognitive Performance
Bibliographic record
Abstract
Objective: Poor physical performance and negative affect (NA) have both been linked to worse executive function. However, little is understood about the relationship between trait NA and physical performance, particularly in older adults. It is possible that NA, and its motivational properties, plays a role in the relationship between executive function and physical decline. Hierarchical multiple regression was thus used to examine whether and the extent to which NA traits and executive function contributed to performance on the short performance physical battery (SPBB), while adjusting for the demographic factors of age and socioeconomic status (income and education). Method: 32 community-dwelling older adults (Mage = 69 years, SD = 5.4) participated in the Maine Understanding Sensory and Cognition (MUSIC) project. Exclusion criteria included scores of >11 on the Geriatric Depression Scale, scores of <19 on the Montreal Cognitive Assessment, diagnosis of a neurodegenerative disease, severe mental illness or a stroke within the last year. The Positive Affect and Negative Affect Schedule (PANAS) measured trait NA. The Trail Making Test (TMT Trails A and B) measured components of attention/processing and executive function. Results: NA associated with significantly worse TMT performance, ps < .001. Regression analyses indicated that socioeconomic status,TMT, and NA each significantly predicted SPPB performance, accounting for 56.9% of the variance. Conclusions: Results replicated other research that has linked SPPB and TMT performance, and extended these findings by investigating whether NA and sociodemographic factors contributed to physical performance. Our findings suggest that trait NA plays a role in poorer physical and executive function performance.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".