Distinguishing Perceived Competence and Self-Efficacy: An Example From Exercise
Bibliographic record
Abstract
UNLABELLED: This article examined the conceptual and statistical distinction between perceived competence and self-efficacy. Although they are frequently used interchangeably, it is possible that distinguishing them might assist researchers in better understanding their roles in developing enduring adaptive behavior patterns. Perceived competence is conceived in the theoretical framework of self-determination theory and self-efficacy is conceived in the theoretical framework of social-cognitive theory. PURPOSE: The purpose of this study was to empirically distinguish perceived competence from self-efficacy for exercise. METHOD: Two studies evaluated the independence of perceived competence and self-efficacy in the context of exercise. Using 2 extant instruments with validity and reliability evidence in exercise contexts, the distinctiveness of the 2 constructs was assessed in 2 separate samples (n = 357 middle-aged sedentary adults; n = 247 undergraduate students). RESULTS: Confirmatory factor analysis supported the conceptual and empirical distinction of the 2 constructs. CONCLUSIONS: This study supports the conceptual and statistical distinction of perceived competence from perceived self-efficacy. Applications of these results provide a rationale for more precise future theorizing regarding their respective roles in supporting initiation and maintenance of health behaviors.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".