Use of Linguistic Communication Strategies (Hedges and Intensifiers) in Simulated Pharmacy Education Shared Decision-Making
Bibliographic record
Abstract
OBJECTIVE: Hedges and intensifiers are linguistic strategies that are used to indicate respect for an individual's face needs, particularly their desire to feel autonomous or part of a social group. This study aimed to investigate the use of such linguistic strategies by pharmacy students and their impact on communication grades in an Objective Structured Clinical Examination, focusing on shared decision-making and uptake of pharmacists' recommendations by patients and prescribers. METHODS: An analytical observational retrospective cohort of Objective Structured Clinical Examination videos across poor, average, and good grades was conducted. Underpinned by politeness theory and using summative content analysis and statistical analysis, the use of hedges and intensifiers was identified, mapped, and compared across communication grades. RESULTS: Overall, students used more hedges than intensifiers when interacting with physicians (1253 vs 565) and patients' carers (2026 vs 369). The most common hedges were modal auxiliary verbs (27.5%), whereas the most common intensifiers were high-strength adverbs (47%). Students who were marked as good communicators were seen to use less hedges when speaking with carers than physicians (median 42 vs 29) vs students who were marked with poor communication skills (median 42 vs 38). CONCLUSION: Overall, pharmacy students tend to hedge when making recommendations. Students who were marked highly by examiners showed differences in the number of hedges between the 2 interlocutors, whereas students who were marked as poor communicators used similar language when talking to the patient or the physicians. Study findings provide insight into links between grading and linguistic strategies used and could inform innovative applied linguistic-based communication training programs for students and examiners, which could lead to preparing graduates to utilize linguistic strategies to communicate pharmacist-led recommendations in the workplace.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".