An Interprofessional Communication Training Program to Improve Nurses’ Ability to Communicate With Stroke Patients With Communication Disorders
Bibliographic record
Abstract
PURPOSE: The aim of the study was to evaluate change in nurses' knowledge, attitudes, and behaviors after an Interprofessional (IP) Communication Training Program designed to improve nurses' ability to communicate with stroke rehabilitation inpatients with communication disorders. DESIGN/METHODS: A quasi-experimental longitudinal study was conducted to evaluate an IP Communication Training Program consisting of a full-day and booster workshop and speech language pathologist support for nurses. The Communication Impairment Questionnaire and the Providers Interactional Comfort Survey were collected prior to and 3 months and 1 year postintervention. Focus groups assessing program acceptability were conducted. RESULTS: Forty-six nurses participated. The Communication Impairment Questionnaire significantly improved postintervention (p = .001), but not at 1 year follow-up (3.76, p = .123). The Providers Interactional Comfort Survey significantly improved postintervention (p = .001) and remained significant 1 year later (8.84, p = .03). Focus groups indicated high acceptability and reported that the education increased their confidence and reduced their frustration. CONCLUSION: Nurses' attitudes toward and knowledge about communication strategies improved, which enhances their ability to care for stroke patients with communication disorders. CLINICAL RELEVANCE: Interdisciplinary workshops and speech language pathologist support appear beneficial to nurses working with patients with communication disorders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".