Can incorporating behaviour change principles make clinical practice guidelines and health policies more impactful in change public health?
Bibliographic record
Abstract
Abstract Background We now live in world of ever-increasing amounts of evidence and information. In spite of the use of high-quality methods to synthesise this information, clinical practice guidelines and health policies often fail to impact public health. Canada has started to develop a series of guidelines and policies which have incorporated a behaviour change perspective into their development to fast track their implementability and their impact on public health. For example, the Canadian Association of Cardiovascular Prevention and Rehabilitation (CACPR)'s new Cardiovascular Rehabilitation Guidelines are being built using key integrated knowledge translation and behaviour change theories to develop recommendations which not only target the what to do but, who, and how this needs to be done. Another example is Canada's new Food Guide, which is unique in the fact that it has taken a behaviour-oriented perspective, rather than a macro and micronutrient path. Both of these examples leverage basic behaviour change principles to get multiple stakeholders to increase their uptake and usage of these knowledge products. Results Both the CACPR guidelines and the Food Guide are still relatively new, so their uptake and impact is not currently measurable. However, there is a monitoring plan which will evaluate these aspects. That being said, there is some evidence that both of these products have been well received by their respective communities. Conclusions Taking a behaviour change perspective in the development and delivery of guidelines and policy, especially health policy, has the potential to positively engage more stakeholders in the process, and fast track their impact on public's health. Ultimately, more evidence is needed to define the optimal way to do this.
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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 | 0.229 | 0.397 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".