Reference to Patients in Nurse Shift Handover Meetings: Exploring the Dynamics of Referring Expressions
Bibliographic record
Abstract
This paper investigates the dynamics of referring expressions in hospital nurse handover meetings when discussing patients. We apply the methods of the Variable Length Markov Chain (VLMC) and network analyses to model the use of referring expressions and evaluate relationships between them. The models reveal second-order dependencies emerging for metonymy and noun phrases. Specifically, metonymy shows a greater association with the beginning of a reference, particularly in the context of other metonymies. In contrast, noun phrases tend to be more strongly associated with later points in the reference. Further, we introduce the notion of referential typicality, which measures the conformity of sequences of referring expressions to anticipated patterns. We show, for example, that consecutive noun phrases fall outside the typical pattern, whereas metonymical sequences and sequences of pronouns are highly typical. The transitions from metonymy or nouns to pronouns also closely align with a highly typical pattern. Using a Generalized Additive Model (GAM), we then track the overall evolution of referential typicality throughout the duration of handover meetings, from their beginning to end. The study reveals a subtle increase in referential typicality towards the end of these sessions, indicating a trend towards more consistent referencing as the discourse unfolds.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".