Distinguishing among perceived control, perceived difficulty, and self‐efficacy as determinants of intentions and behaviours
Bibliographic record
Abstract
Perceptions of control hold a dominant position in social cognitive theories yet there is a lack of conceptual and empirical clarity regarding what kind of control is most associated with particular behaviours. Three prominent types of control are perceived control (PC), perceived difficulty (PD), and perceived confidence or self-efficacy (SE) for performing the desired behaviour. Three studies are presented with a primary goal of distinguishing PC, PD, and SE from each other, and a secondary goal of determining which of the three is the superior predictor of health-related intentions and behaviours. The first study replicates earlier work by Trafimow et al. (2002) distinguishing the three constructs for reading 1, 30, and 100 pages, and extends it to exercising one, two, four, and six times per week and also to predicting intentions to exercise 4 days per week and behaviour 1 week later. The second study examines the predictive capability of the three constructs for intentions to floss one's teeth everyday and to eat 5-10 servings of fruits and vegetables everyday and subsequently on behaviour assessed 7 days later. The third study reports a meta-analysis of the relative influence of PC, PD, and SE on behaviours when defined in conceptually consistent ways. The results of all three studies support the conceptual and empirical distinction of PC, PD, and SE and the superiority of SE as a predictor of health behaviours and intentions.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".