The confounded self-efficacy construct: conceptual analysis and recommendations for future research
Bibliographic record
Abstract
Self-efficacy is central to health behaviour theories due to its robust predictive capabilities. In this paper, we present and review evidence for a self-efficacy-as-motivation argument in which standard self-efficacy questionnaires - i.e., ratings of whether participants 'can do' the target behaviour - reflect motivation rather than perceived capability. The potential implication is that associations between self-efficacy ratings (particularly those that employ a 'can do' operationalisation) and health-related behaviours simply indicate that people are likely to do what they are motivated to do. There is some empirical evidence for the self-efficacy-as-motivation argument, with three studies demonstrating causal effects of outcome expectancy on subsequent self-efficacy ratings. Three additional studies show that - consistent with the self-efficacy-as-motivation argument - controlling for motivation by adding the phrase 'if you wanted to' to the end of self-efficacy items decreases associations between self-efficacy ratings and motivation. Likewise, a qualitative study using a thought-listing procedure demonstrates that self-efficacy ratings have motivational antecedents. The available evidence suggests that the self-efficacy-as-motivation argument is viable, although more research is needed. Meanwhile, we recommend that researchers look beyond self-efficacy to identify the many and diverse sources of motivation for health-related behaviours.
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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 | 0.076 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".