Personal trainers and their clients: Preliminary evidence of the role of relational efficacies in predicting self-efficacy and intention to return
Bibliographic record
Abstract
In situations of proxy-agency, relational efficacies (e.g., proxy-efficacy, RISE beliefs, other efficacy) are theorized to impact self-efficacy (SE) and the effort expended in joint tasks. Personal trainers (PT) are proxy-agents and are in an interesting position balancing development of their clients' capabilities while also fostering a long-term commitment from clients. As such, they provide an important context in which to study relational efficacies and their impact on other variables. The purpose of this study was to examine (1) whether PT other-efficacy and client RISE beliefs contribute to client SE and (2) whether relational efficacies contribute to clients' decisions to continue the with their PT. PT and client pairs completed measures of relational and self-efficacies after sessions 1 and 5 of a 5-session block. Clients also reported intention to continue with their PT after session 5. Hierarchical regression showed PT other efficacy (R2ch=.22) and clients' RISE beliefs (R2ch=.17, ps< .04) to be separate significant predictors of clients' SE with both being positively associated with SE. A second model showed that after controlling for client SE, proxy-efficacy and RISE beliefs were additive predictors of clients' intentions to continue to work with their PT (R2ch=.49, p
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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".