Interprofessional communication with hospitalist and consultant physicians in general internal medicine: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Studies in General Internal Medicine [GIM] settings have shown that optimizing interprofessional communication is important, yet complex and challenging. While the physician is integral to interprofessional work in GIM there are often communication barriers in place that impact perceptions and experiences with the quality and quantity of their communication with other team members. This study aims to understand how team members' perceptions and experiences with the communication styles and strategies of either hospitalist or consultant physicians in their units influence the quality and effectiveness of interprofessional relations and work. METHODS: A multiple case study methodology was used. Thirty-one semi-structured interviews were conducted with physicians, nurses and other health care providers [e.g. physiotherapist, social worker, etc.] working across 5 interprofessional GIM programs. Questions explored participants' experiences with communication with all other health care providers in their units, probing for barriers and enablers to effective interprofessional work, as well as the use of communication tools or strategies. Observations in GIM wards were also conducted. RESULTS: Three main themes emerged from the data: [1] availability for interprofessional communication, [2] relationship-building for effective communication, and [3] physician vs. team-based approaches. Findings suggest a significant contrast in participants' experiences with the quantity and quality of interprofessional relationships and work when comparing the communication styles and strategies of hospitalist and consultant physicians. Hospitalist staffed GIM units were believed to have more frequent and higher caliber interprofessional communication and collaboration, resulting in more positive experiences among all health care providers in a given unit. CONCLUSIONS: This study helps to improve our understanding of the collaborative environment in GIM, comparing the communication styles and strategies of hospitalist and consultant physicians, as well as the experiences of providers working with them. The implications of this research are globally important for understanding how to create opportunities for physicians and their colleagues to meaningfully and consistently participate in interprofessional communication which has been shown to improve patient, provider, and organizational outcomes.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".