Describing the Clinical Communication Space through a Model of Common Ground: 'you don't know what you don't know'.
Bibliographic record
Abstract
Common ground refers to the knowledge shared by two communicating parties to enable communication to occur. We suggest that common ground could enhance collaborative care delivery by serving as the linkage between different healthcare team members. Despite research describing the importance of common ground to facilitate communication, little is known about how common ground forms, moments where it is necessary, and barriers to achieving it. To address this shortcoming we studied collaborative care delivery in two settings and then used Grounded Theory methodology to develop a model of common ground. The model contains four main concepts: moments of common ground, barriers to common ground, fabric of common ground, and consequences of weak common ground. Our findings show that common ground is multi-dimensional with both static and dynamic aspects. The results from this paper help us to better understand collaborative care delivery and how to design information and communication technologies to support it.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".