Using behaviour change principles in the development and adoption of health policies
Bibliographic record
Abstract
Abstract Issue/problem We now live in world of ever-increasing amounts of evidence and information. Unfortunately, high quality evidence is not always incorporated into policy documents and can be ignored by policy makers when making decisions. Description of the problem Canada recently released its Healthy Eating Strategy, a comprehensive policy which covers a number of aspects, including Canada’s new Food Guide. The Food Guide is rooted in both nutritional and behavioural evidence. It is unique in the fact that it has taken a behaviour-oriented perspective, rather than a macro and micronutrient path. In addition, to incorporating a behavioural perspective into the policy there is a concerted effort to leverage basic behaviour change principles to get the healthcare community to increase their uptake and usage of the food guide. Furthermore, the same principles are being leveraged to ensure that policy makers and members of the government continually reinvest and push the food guide forward as new evidence is generated. Results Though the Healthy Eating Strategy is still relatively new, there has been substantial policy movement on a number of the areas it will tackle. For the Food Guide, this was only released at the start of 2019, so its 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 the Food Guide, and the messaging around it, has been well received. Lessons Taking a behaviour change perspective in the development and delivery of policy, especially health policy, has the potential to positively engage more stakeholders in the process. 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 | 0.184 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.011 | 0.017 |
| 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".