Towards a theory of intentional behaviour change: Plans, planning, and self‐regulation
Bibliographic record
Abstract
PURPOSE: Briefly review the current state of theorizing about volitional behaviour change and identification of challenges and possible solutions for future theory development. METHOD: Review of the literature and theoretical analysis. RESULTS: Reasoned action theories have made limited contributions to the science of behaviour change as they do not propose means of changing cognitions or account for existing effective behaviour change techniques. Changing beliefs does not guarantee behaviour change. The implementation intentions (IMPs) approach to planning has advanced theorizing but the applications to health behaviours often divert substantially from the IMPs paradigm with regard to interventions, effects, mediators and moderators. Better construct definitions and differentiations are needed to make further progress in integrating theory and understanding behaviour change. CONCLUSIONS: Further progress in theorizing can be achieved by (a) disentangling planning constructs to study their independent and joint effects on behaviour, (b) progressing research on moderators and mediators of planning effects outside the laboratory and (c) integrating planning processes within learning theory and self-regulation theory.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".