Mobilizing knowledge in behaviour change to promote health; the case of the Behaviour Change Wheel
Bibliographic record
Abstract
Abstract Issue/problem Over the past decades, researchers from many fields have built an impressive body of knowledge regarding behaviour change. However, the use of this knowledge for accurately designing/delivering/executing behaviour change programs is challenging for many public health practitioners. Description of the problem To support effective knowledge mobilization in behaviour change and to build a coherent and useful body of scientific evidence, leading researchers in behavioural sciences have developed and refined a number of tools for designing interventions. Among these tools, the Behaviour Change Wheel (BCW) was built on an impressive effort to synthetize available evidence regarding intervention development frameworks, behaviour change theories, and behaviour change techniques. However, studies highlighted some issues associated with the use of these methodological innovations. Working with our public health partners in the field of health communication, we realized that applying models such as the BCW is far from being mundane practices. To support optimal knowledge mobilization in behavioural sciences, we are developing a research agenda to understand perceptions and motivations of public health practitioners toward innovations such as the BCW and to develop and evaluate knowledge mobilization strategies. Results The initiative will contribute to the development of new scientific knowledge regarding mechanisms underlying effective knowledge mobilization in behaviour change and will further support the adoption of these evidence-based practices within the field of public health. Lessons Although rapidly adopted by the community of researchers, it is not clear whether or not public health practitioners would be as willing, or capable of using the BCW to design and deliver programs. Issues regarding knowledge mobilization in behaviour change should be addressed to improve the uptake of this knowledge in practice.
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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 | 0.056 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.081 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".