Interprofessional Relationships in the Field of Obesity: Data from Canada
Bibliographic record
Abstract
Background: While it is generally acknowledged that an interprofessional approach is necessary to treat and prevent obesity, there have been few empirical studies examining the working relationships of professionals in the obesity field.Methods: In this article social network analysis is used to examine the working relationships of 111 attendees, representing eleven different health professions, at the first National Obesity Summit in Canada. We assessed the extent of engagement in interprofessional relations across four activities: discussion, gathering information, providing care, and conducting research. We also examined attitudes toward interprofessional practice.Findings: On average, respondents reported that approximately 75% of the people they work with are from other professions. Attitudes toward interprofessional practice were generally positive, and did not vary significantly across professions. Interestingly, attitudes were not related to actual interprofessional relations in our sample. In terms of work type, we found that respondents who were engaged in both clinical and research work had the largest networks and had the highest percentage of interprofessional contacts in their discussion and research networks.Conclusions: Overall, the results suggest that within our sample of professionals working in the field of obesity, interprofessional practice is held in high regard as a concept. The results also suggest that members of professions that combine both research and clinical work are most likely to engage in interprofessional relationships. This article illustrates the utility of social network analysis to assess the extent of interprofessional relationships among those working in a particular healthcare field.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".