Communication and interprofessional collaborative practice: collective sensemaking work
Bibliographic record
Abstract
Communication is often listed as a key ingredient for effective interprofessional collaborative practice (ICP) in health care, and is frequently conceptualized as information transmission.Without denying this important function, I propose to problematize communication as constitutive, social action.This allows us to understand ICP as a process of collective sensemaking that emerges in and through communicative action.Taking seriously the term practice in ICP, this ethnography adopts a practice theory lens, informed by ethnomethodology and interaction analysis, to examine and characterize a specific practice: the interprofessional patient case review in daily team rounds.This practice is seen to be collectively enacted in routines and socio-materially embedded in other practices.The study draws on observations and audio recordings of 4,000 patient case reviews from 120 daily rounds of 3 interprofessional acute care teams in a university hospital in Western Canada.Variations in practice within and across the teams prompted three interrelated and emergent analyses.First, I show the importance of introductions to case reviews as salience-framing resources that emplot the patient's situation on the care trajectory for listening team members, thereby underscoring the essential gatekeeping role played by charge nurses.I argue an interprofessional performance has to do with heedful interrelating, discernable in interaction as displayed mindfulness of difference and an attentiveness to expressions of uncertainty.Second, I recast the question of medical dominance in terms of authorship, and consider its interactional enactment.Here, the presence of a medical representative changes the focus of sensemaking work as well as the audience for whom talk is designed.Third, I examine potential stabilizers of sensemaking practice in the context of shifting team composition.Practice is stabilized and continuity of the patient's story maintained through the participation of multiple authors or "story porters," both human and non-human, shedding new light on IP and multivocality.These findings inform a model of IP sensemaking in the patient case review, especially highlighting the key role of the hybrid nurse-and-notes actor and the importance of sensitivity to expressions of uncertainty.The model could be useful in teaching interprofessional practice to students and practitioners.
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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 | 0.036 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.092 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.005 |
| 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".