Association between Self-Efficacy and Health Behaviour in Disease Control: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Self-efficacy is defined as his or her belief of their capacity to produce specific performance attainments where represent the ability of positive and negative control over their own motivation, behavior, and social environment. Interventions to increase self-efficacy are a potentially effective way of changing health behavior towards attractive results, however the subject has not been systematically reviewed. This review aims to assess the relationship between self-efficacy and change in good health behaviour.METHODS: A comprehensive and extensive search of four bibliographic data bases was conducted for papers reporting health promotion and educational interventions that explicitly targeted self-efficacy in order to change health behaviour in achieving better disease control.RESULTS: Out of 314 studies, 13 were included in the review. Of these, 8 were found to have significant association between self-efficacy and specific health behaviours. However, 5 studies failed to show any significant prediction towards health behavior. Another 6 studies showed either significant mediation effect or indirect relationship of self-efficacy and health behaviours.CONCLUSIONS: Self-efficacy appears to be an important psychosocial construct that may directly or indirectly affect health behaviour to control diseases. Self-efficacy may also function as a link between effective health promotion and educational interventions and health behaviour change in disease control.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".