Communication Skills After Graduation: Opinions of Polish Healthcare Professionals
Bibliographic record
Abstract
Antonina Doroszewska,1 Iwona Drozdowska,1 Maciej Mikołajczak,1 Adam Jasiura,2 Joanna Zawanowska1 1Department of Medical Communication, Medical University of Warsaw, Warsaw, Poland; 2Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, PolandCorrespondence: Iwona Drozdowska, Department of Medical Communication, Medical University of Warsaw, Litewska 16 Street, Warsaw, 00-575, Poland, Tel +48221169270, Email iwona.drozdowska@wum.edu.plBackground: Compulsory education of communication skills has begun to develop in Poland over the past several years. The aim of this study was to explore perception of preparation by the universities and self-assessment in communication skills among Polish doctors, nurses and midwives.Methods: The research was based on an online survey examining 22 communication skills. A list of communication skills was created based on the recommendations for teaching medical communication described in the Calgary-Cambridge model. Data collection took place from March to July 2022. The sample consisted of a variety of health professions, including physicians, nurses and midwives.Results: The results show that medical school graduates who took part in the survey rated their preparation in communication skills as low and the number of hours dedicated to communication training as insufficient. The most challenging aspects of communication skills include dealing with patient aggression, handling patients who express complaints, grievances, or expectations and discussing non-medical treatments with patients. The surveyed nurses and midwives rated their overall preparation significantly higher than physicians and felt strong in showing empathy. Doctors in turn assessed their task-oriented communication skills as much better.Conclusion: The respondents emphasized the importance of communication skills training. Our findings underscore the urgent need for structured, longitudinal communication training within medical education, supported by systemic changes at the levels of policy, curriculum design, and faculty development. Communication skills must be prioritized as core clinical competencies across all health professions, not treated as peripheral elements of medical education and ideally should be continued throughout one’s medical career and postgraduate education. Particular emphasis should be placed on teaching how to manage patient aggression, address complaints, and communicate with patients who pursue alternative medicine and not evidence based treatment. These results may also assist universities in other countries, where medical curricula are being implemented.Keywords: communication skills, self-assessment, undergraduate medical students, assessment of teaching communication skills
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.009 |
| 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 | 0.001 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".