Assessing Physician’s Motivational Communication Skills: 5-Step Mixed Methods Development Study of the Motivational Communication Competency Assessment Test
Bibliographic record
Abstract
BACKGROUND: Training physicians to provide effective behavior change counseling using approaches such as motivational communication (MC) is an important aspect of noncommunicable chronic disease prevention and management. However, existing evaluation tools for MC skills are complex, invasive, time consuming, and impractical for use within the medical context. OBJECTIVE: The objective of this study is to develop and validate a short web-based tool for evaluating health care provider (HCP) skills in MC-the Motivational Communication Competency Assessment Test (MC-CAT). METHODS: Between 2016 and 2021, starting with a set of 11 previously identified core MC competencies and using a 5-step, mixed methods, integrated knowledge translation approach, the MC-CAT was created by developing a series of 4 base cases and a scoring scheme, validating the base cases and scoring scheme with international experts, creating 3 alternative versions of the 4 base cases (to create a bank of 16 cases, 4 of each type of base case) and translating the cases into French, integrating the cases into the web-based MC-CAT platform, and conducting initial internal validity assessments with university health students. RESULTS: The MC-CAT assesses MC competency in 20 minutes by presenting HCPs with 4 out of a possible 16 cases (randomly selected and ordered) addressing various behavioral targets (eg, smoking, physical activity, diet, and medication adherence). Individual and global competency scores were calculated automatically for the 11 competency items across the 4 cases, providing automatic scores out of 100. From the factorial analysis of variance for the difference in competency and ranking scores, no significant differences were identified between the different case versions across individual and global competency (P=.26 to P=.97) and ranking scores (P=.24 to P=.89). The initial tests of internal consistency for rank order among the 24 student participants were in the acceptable range (α=.78). CONCLUSIONS: The results suggest that MC-CAT is an internally valid tool to facilitate the evaluation of MC competencies among HCPs and is ready to undergo comprehensive psychometric property analyses with a national sample of health care providers. Once psychometric property assessments have been completed, this tool is expected to facilitate the assessment of MC skills among HCPs, skills that will better support patients in adopting healthier lifestyles, which will significantly reduce the personal, social, and economic burdens of noncommunicable chronic diseases.
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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 | 0.033 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".