Enhancing the translation of health behaviour change research into practice: a selective conceptual review of the synergy between implementation science and health psychology
Bibliographic record
Abstract
Health psychology is at the forefront of developing and disseminating evidence, theories, and methods that have improved the understanding of health behaviour change. However, current dissemination approaches may be insufficient for promoting broader application and impact of this evidence to benefit the health of patients and the public. Nevertheless, behaviour change theory/methods typically directed towards health behaviours are now used in implementation science to understand and support behaviour change in individuals at different health system levels whose own behaviour impacts delivering evidence-based health behaviour change interventions. Despite contributing to implementation science, health psychology is perhaps doing less to draw from it. A redoubled focus on implementation science in health psychology could provide novel prospects for enhancing the impact of health behaviour change evidence. We report a Health Psychology Review-specific review-of-reviews of trials of health behaviour change interventions published from inception to April 2020. We identified 34 reviews and assessed whether implementation readiness of behaviour change interventions was discussed. We then narratively review how implementation science has integrated theory/methods from health psychology and related discipline. Finally, we demonstrate how greater synergy between implementation science and health psychology could promote greater follow-through on advances made in the science of health behaviour change.
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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 | 0.034 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".