Applying behavioural science to improve physicians’ ability to help people improve their own health behaviours
Bibliographic record
Abstract
Abstract Issue/problem Poor health behaviours are at the centre of most non-communicable chronic diseases and account for a significant amount of morbidity and mortality. Healthcare professionals, and especially physicians, are in a unique position to be able to positively influence their patients and aid them in changing poor health behaviours. However, most physicians report having low confidence or a lack of skills to effectively achieve this. Description of the problem The main approach that physicians take to influence their patients’ poor health behaviours is to provide them with advice and evidence about the impact of the poor health behaviours. This strategy has been shown to have limited impact on changing patient behaviour. As such, there is a need to develop effective interventions that target changing physician health behaviour counselling behaviours, effectively, a behaviour change intervention for physicians so that they are better at helping patients change their behaviour. Results Using a structured stakeholder-oriented approach (the ORBIT model for developing behavioural interventions) we have systematically developed a robust behaviour change-based continuing medical education curriculum (leveraging motivational communication), and online assessment tool to improve physician competency. These were developed by a pan-Canadian team with notable international input through the IBTN. Lessons The use of a structured stakeholder-driven process, we have developed an intervention which seems to have greater relevancy to the target audience, lead to greater engagement, and a higher probability of implementation than a researcher led approach. Whilst the studies are still ongoing, it is anticipated that this intervention will be able to dramatically improve the health of individuals through effective health behaviour change interventions by healthcare professionals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".