Bibliographic record
Abstract
Exercise adherence remains a problem. Drawing from Lewin's iconic formula about understanding human behavior, examination of both personal and environmental factors appear warranted as we try to identify factors to help us to 'keep on truckin'. On the personal side, autonomous regulation of physical activity has been positively associated with the frequency of being active (Teixeira et al., 2012). In terms of environment, it has been reported that perceived competence or success determines subsequent motivation, and that success is more motivating than failure (Losier & Vallerand, 1994). However, there is limited research examining the interaction of individual motivation and performance outcome on one's intention to return to the popular activity of running. The purpose of this study was to examine how an individual's motivation and satisfaction with a race-day outcome were related to the intention to return to running. Individuals (n=60, Mage= 43.8) participating in a community fun run completed the BREQ (Wilson et al., 2006) to assess autonomous motivation one week before the race, then completed post-race, the individual performance subscale adapted from the ASQ (Reimer & Chelladurai, 1998) to assess race outcome satisfaction and an intention to return to running measure. Results from the hierarchical regression revealed that runners with higher autonomous motivation reported a greater intention to return to running regardless of satisfaction with race-day outcome. These initial findings indicate that personal factors such as autonomous regulation may have more impact on future intention to run than environmental factors such as satisfaction with a race-day outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".