Multi-professional education in diabetes
Bibliographic record
Abstract
Multi-professional education (MPE) is a forward-looking educational strategy that is both innovative and clinically needed. The primary goal of this program was to assess the potential of MPE in diabetes care. Objectives included knowledge increase in type 2 diabetes care and examining the effect of uni-professional versus multi-professional small groups on learning outcomes. A total of 61 baccalaureate nursing students, four pharmacy students and 56 family practice residents participated in a half-day program. Participants were randomly assigned to multi-versus uni-professional groups. A questionnaire assessing knowledge, attitudes and perceived role responsibilities was anonymously completed in a pre-test/post-test manner. The program was delivered in two sessions that combined larger and small group activities. Quantitative data were analyzed using repeated measures Anova/Manova, Wilcoxon signed rank and Pearson chi-square tests. Participants and educators showed interest in multi-professional education. While no change in knowledge was found, moderate changes in attitudes (with a lessening of attitudinal differences) and significant changes in perceptions of role (from uni-professional responsibility to shared responsibilities) were noted. Nurses in uni-professional groups demonstrated the greatest attitudinal and role perception changes. The contribution of the patient as teacher was prominent. Further exploration and rigorous analysis of the utility of MPE in diverse settings is warranted.
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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 | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".