Improving Health Literacy Training and Communication Competencies for Health Professionals in Austria and Canada: A Comparative Study
Bibliographic record
Abstract
Background Canada is a leader in health promotion, public health, interprofessional curricula, intercultural competencies and has a longer history of health literacy (HL) developments. Austria is in the early stages of HL capacity building and developing communication training for health professionals treating people in vulnerable situations. The primary aim of our project is to analyze the education of health professionals (medical, nursing and pharmacy students) regarding HL, intercultural and interprofessional communication skills and patient-centered interactions with underserved populations in Austria and Canada. The importance of communication in healthcare has increased significantly during the COVID-19 pandemic; thus the question of how communication training has changed in the light of the pandemic is also explored. Methods The study design included four methods: a literature review, a document analysis, a curricula survey, and expert interviews. Results Recognizing the importance of HL and improved quality of health communication; the Austrian government along with educational institutions have looked abroad to identify best practises, e.g., from Canada. Moreover, a national train-the-trainer program was developed but is yet to be integrated in all universities. Expert interviews and curricula surveys as well as a document analysis indicate that communication training varies by province in Austria and Canada in terms of both course content and longitudinal integration. COVID-19 has changed clinical communication practises and hindered training for young professionals. Conclusion The development of communication curricula and HL competencies is a long and continuous process in both countries. Publication History Article published online: 22 August 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".