Relational Efficacy Beliefs in Physical Activity Classes: A Test of the Tripartite Model
Bibliographic record
Abstract
This study explored the predictive relationships between students' (N = 516, Mage = 18.48, SD = 3.52) tripartite efficacy beliefs and key outcomes in undergraduate physical activity classes. Students reported their relational efficacy perceptions (i.e., other-efficacy and relation-inferred self-efficacy, or RISE) with respect to their instructor before a class, and instruments measuring self-efficacy, enjoyment, and effort were administered separately following the class. The following week, an independent observer assessed student achievement. Latent variable path analyses that accounted for nesting within classes revealed (a) that students were more confident in their own ability when they reported favorable other-efficacy and RISE appraisals, (b) a number of direct and indirect pathways through which other-efficacy and RISE predicted adaptive in-class outcomes, and (c) that self-efficacy directly predicted enjoyment and effort, and indirectly predicted achievement. Although previous studies have examined isolated aspects within the tripartite framework, this represents the first investigation to test the full range of direct and indirect pathways associated with the entire model.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".