Moderators of the Exercise/Feeling‐State Relationship: The Influence of Self‐Efficacy, Baseline, and In‐Task Feeling States at Moderate‐ and High‐Intensity Exercise
Bibliographic record
Abstract
The present study examined the moderating influence of self‐efficacy, baseline feeling states, and in‐task feeling states on exercise‐related feeling‐state changes at moderate‐ and high‐intensity exercise. Physically active females (N= 60) participated in 1 of 5 conditions: (a) attention control for 30 min, (b) exercise at 50% heart rate reserve (HRR) for 15 min, (c) exercise at 50% HRR for 30 min, (d) exercise at 85% HRR for 15 min, and (e) exercise at 85% HRR for 30 min. The Exercise‐Induced Feeling Inventory (EFI; Gauvin & Rejeski, 1993) was completed pre‐, during, and post‐exercise, while self‐efficacy was completed pre‐exercise. Multilevel modeling (Bryk & Raudenbaush, 1992) revealed that pre‐exercise self‐efficacy and in‐task tranquility moderated the change in tranquility for high‐intensity exercise. Furthermore, baseline feeling states moderated the change in all 4 feeling states. It is recommended that baseline and in‐task feeling states and self‐efficacy be considered when examining high‐intensity exercise.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".