Behaviour change interventions: getting in touch with individual differences, values and emotions
Bibliographic record
Abstract
Systematic reviews and meta-analyses suggest that behaviour change interventions have modest effect sizes, struggle to demonstrate effect in the long term and that there is high heterogeneity between studies. Such interventions take huge effort to design and run for relatively small returns in terms of changes to behaviour.So why do behaviour change interventions not work and how can we make them more effective? This article offers some ideas about what may underpin the failure of behaviour change interventions. We propose three main reasons that may explain why our current methods of conducting behaviour change interventions struggle to achieve the changes we expect: 1) our current model for testing the efficacy or effectiveness of interventions tends to a mean effect size. This ignores individual differences in response to interventions; 2) our interventions tend to assume that everyone values health in the way we do as health professionals; and 3) the great majority of our interventions focus on addressing cognitions as mechanisms of change. We appeal to people's logic and rationality rather than recognising that much of what we do and how we behave, including our health behaviours, is governed as much by how we feel and how engaged we are emotionally as it is with what we plan and intend to do.Drawing on our team's experience of developing multiple interventions to promote and support health behaviour change with a variety of populations in different global contexts, this article explores strategies with potential to address these issues.
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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 | 0.035 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".