Training physicians in motivational communication to improve behaviour change counselling competency: A behavioural medicine approach to NCD prevention and management
Bibliographic record
Abstract
Abstract Background Despite the importance of changing health behaviours in the context of preventing and managing non-communicable chronic diseases (NCD's), physician use of evidence-based behaviour change counselling (BCC) is low, and BCC skills competency is generally poor. Motivational communication (MC) is a patient-centred, evidence-based BCC approach used by healthcare providers, designed to increase patient motivation to adopt a healthy lifestyle. MC-based approaches improved a range of health behaviours (smoking, diet, physical activity) in patients with NCDs, leading to increased demand for physician training. Despite the widespread dissemination of training programs, data on their efficacy in achieving competency among physicians is limited. This is likely due to a lack of consensus on the core communication competencies to be achieved, and in the absence of acceptable, valid and reliable tools to measure skill acquisition. Results Using an integrated knowledge translation (iKT) approach that engaged 199 international physicians, behaviour change experts and health administrators, we have identified 11 core evidence-based communication competencies that physicians should acquire in the context of NCD prevention/management. They have been incorporated into a basic 4 hr face-to-face MC training program called “LEARN THE BASICs”. To assess MC competency, we have also developed a reliable, engaging, efficient, 'user-friendly' case-based digital assessment tool called the MC-Competency Assessment Test (MC-CAT). Conclusions Strategies for optimizing and tailoring this program, including finding the most cost-effective training dose, the impact of supplemental training components (e.g., in person vs. digital coaching; booster sessions), and delivery modes (e.g., face-to-face vs digital/online), will be discussed in the context of optimizing implementation success.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".