Moving on from structured communication to collaboration: a communication schema for interprofessional teams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: We describe a conceptual framework for an approach towards an effective and collaborative communication strategy among interprofessional health care team members.Methods: Interprofessional health care team members apply different communication techniques to share healthcare data. They are often not taught how to recognize and respond to the challenges carried by emotions and pressure to conform among team members. Those two factors have the potential to affect the quality and outcome of communication with a resulting suboptimal clinical decision-making. Here, we developed a Communication Schema that addresses the three critical concepts that are embedded in any conversation, namely: 1 – understanding the contribution of social interaction in teamwork, 2 – recognizing and managing triggered emotions, and 3 – effective inquiries and exploration of points of view.Implications: We call on extending the traditional view of teamwork as a collection of skills to include the reality of interprofessional practice where people working together are influenced by social experience and expectations. The learning conversation schema offers a tool that is sensitive to innate threats to effective communication in working environments and allows its users to circumvent obstacles to optimum shared decision-making.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it