Interprofessional interaction, negotiation and non-negotiation on general internal medicine wards
Bibliographic record
Abstract
Research suggests that health care can be improved and patient harm reduced when health professionals successfully collaborate across professional boundaries. Consequently, there is growing support for interprofessional collaboration in health and social care, both nationally and internationally. Factors including professional hierarchies, discipline-specific patterns of socialization, and insufficient time for teambuilding can undermine efforts to improve collaboration. This paper reports findings from an ethnographic study that explored the nature of interprofessional interactions within two general and internal medicine (GIM) settings in Canada. 155 hours of observations and 47 interviews were gathered with a range of health professionals. Data were thematically analyzed and triangulated. Study findings indicated that both formal and informal interprofessional interactions between physicians and other health professionals were terse, consisting of unidirectional comments from physicians to other health professionals. In contrast, interactions involving nurses, therapists and other professionals as well as intraprofessional exchanges were different. These exchanges were richer and lengthier, and consisted of negotiations which related to both clinical as well as social content. The paper draws on Strauss' (1978) negotiated order theory to provide a theoretical lens to help illuminate the nature of interaction and negotiation in GIM.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".