Impact of an educational intervention combining clinical obesity preceptorship with electronic networking tools on primary care professionals: a prospective study
Bibliographic record
Abstract
BACKGROUND: Primary care providers' (PCPs) attitude toward obesity is often negative, and their confidence level for helping patients manage their weight is low. Continuing professional development (CPD) on the subject of obesity is often based on a single activity using a traditional passive approach such as lectures known to have little effect on performance or patient outcomes. The aim of this study was to evaluate the impact of an educational intervention for obesity management on PCPs' attitude, self-efficacy, practice changes and patient-related outcomes. METHODS: Prospective interventional study with 12 months follow-up. A two-day clinical obesity preceptorship was offered where participants were actively involved in competence building using real-life situations, in addition to electronic networking tools, including a discussion forum and interactive monthly webinars. Thirty-five participants (12 nurses and 23 physicians) from seven Family medicine groups were enrolled. Questionnaires were used to evaluate the impact on primary care nurses' and physicians' attitudes and self-efficacy for obesity management. Practice changes and patient outcomes were evaluated using clinical vignettes, de-identified electronic patient records and qualitative analyses from group interviews. RESULTS: Physicians' general attitude towards patients with obesity was improved (61 ± 22 mm vs 85 ± 17 mm, p < 0.001). Self-efficacy for obesity management and lifestyle counselling were also improved immediately and 1 year after the intervention (all Ps < 0.05). De-identified patient records and clinical vignettes both showed improvement in recording of weight, waist circumference and evaluation of readiness to change lifestyle (all Ps < 0.05) that was confirmed by group interviews. Also, 15% of patients who were prospectively registered for weight management had lost more than 5% of their initial weight at the time of their last visit (P < 0.0001, median follow-up of 152 days). CONCLUSION: A multimodal educational intervention for obesity management can improve PCPs'attitude and self-efficacy for obesity management and lifestyle counselling. This translates into beneficial practice changes and patient-related outcomes. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01385397 . Retrospectively registered, 28 June 2011.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".