Lifestyle counseling in primary care: opportunities and challenges for changing practice
Bibliographic record
Abstract
BACKGROUND: Many patients today have health concerns related to lifestyle factors. This has created a situation where physicians are regularly confronted with the challenge of how to conduct lifestyle counseling with patients. Specific strategies can enable physicians to more effectively navigate this complex area of communication with patients, improving patient response in adopting healthy behaviours and increasing physician satisfaction with this task. AIM: To evaluate the impact of a lifestyle counseling workshop incorporating the motivational enhancement and transtheoretical models upon primary care clinicians' counseling practice patterns, especially communication and counseling skills, and attitudes toward lifestyle counseling. METHOD: This study used a mixed method research design. Forty-three clinicians completed a post-workshop evaluation and identified intended changes to practice following the workshop. Twelve participated in interviews several months later to explore the kinds of changes made and influences upon them. RESULTS: Forty-one (95.3%) questionnaire respondents reported an intention to change their practice. Main changes reported were: asking more questions, listening more, assessing patients' readiness to change, tailoring counseling to patients' readiness to change. They seemed to have acquired and retained new knowledge and most were able to apply the new skills in their practices. Many reported feeling more comfortable and/or confident when interacting with patients in need of lifestyle change. But, time constraints, comfort with current skills, lack of self-efficacy, and fears of missing opportunities to influence patients, moderated participants' ability to adopt and maintain new approaches. CONCLUSIONS: While primary care clinicians can successfully learn specific lifestyle counseling skills and incorporate them into their practice following a two-hour evidence-based workshop, individual, educational and system factors can interfere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".