Team based communication and the healthcare communication space
Bibliographic record
Abstract
PURPOSE: While previous studies have described structural, process and social aspects of the healthcare communication space there is no overall model of it. Such a model is an essential first step to improving the operationalization and management of healthcare communication. The paper aims to discuss these issues. DESIGN/METHODOLOGY/APPROACH: This paper used a case study approach to study team-based communication on a palliative care unit. Non-participant observation, interviews and documents were analyzed using qualitative content analysis. FINDINGS: The analysis developed an overall model of the healthcare communication space that consists of five stages: purpose, practices and workflows, structures, implementation, and the development of common ground to support team-based communication. The authors' findings emphasized that implicit communication remains a predominant means of communication and workflow issues at the individual level are a frequent cause of unnecessary group communication tasks. ORIGINALITY/VALUE: To improve team-based communication we first need to develop protocols that support team communication needs such as loop closing of group communication tasks in order to minimize unnecessary individual communication tasks. We also need to develop common ground at the protocol, document and terminology levels as part of supporting team-based communication.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".