Test of an interprofessional collaborative practice model to improve obesity-related health outcomes in Michigan
Bibliographic record
Abstract
The purpose of the study was to test the effectiveness of an interprofessional collaborative practice (IPCP) education program on clinicians' and students' knowledge and attitudes toward IPCP and to determine the effectiveness of an IPCP weight loss program in two nurse-managed centers. The study team used the Midwest Interprofessional Practice, Education, and Research Center (MIPERC) collaborative practice education program that consists of online learning modules followed by daily huddles and collaborative care planning. The obesity intervention program was implemented by faculty and staff practitioners and students in two clinics with very different patient populations (community residents and college students). Staff/faculty practitioners and students demonstrated statistically significant knowledge gains as a result of online learning modules (Introduction to IPE p < .05; Motivational Interviewing p < .001; Safety Behaviors p < .001; Team Dynamics p < .001). Small, but not statistically significant changes in attitudes toward IPCP were seen with both groups. At program completion, enrolled patients showed statistical significant (p < .001) weight losses and decreases in body mass indices. Other health outcomes showed no significant changes (blood pressure, prevalence of smoking, exercise frequency or duration p > .05). The study demonstrated the potential of an IPCP program to affect weight loss in two populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".