Development of a syllabus for postgraduate respiratory physiotherapy education: the Respiratory Physiotherapy HERMES project
Bibliographic record
Abstract
Best practice in the diagnosis and management of patients with respiratory conditions is now a multidisciplinary effort [1, 2]. Physiotherapists engage in many aspects of the care of patients with respiratory diseases. Across a range of diseases, ages and settings, they carry out highly specialised treatments related to mucus clearance, breathing exercises, invasive and non-invasive mechanical ventilation, exercise training and rehabilitation, as well as reintegration of patients with respiratory disorders. Their tasks span from the neonatal intensive care unit to the palliative care unit of geriatric patients. Over the past decades, respiratory physiotherapists across the world have published research in all these fields feeding into the evidence base that underpins much of the care provided by these professionals. Physiotherapy practice has also evolved over the past few decades. Self-referral by service users (patients) is now possible in approximately half of the European member states of the World Confederation on Physiotherapy [3]. This requires highly trained health professionals capable of assessing, treating, referring and reintegrating patients. In patients with respiratory conditions, this is often performed in the context of a multidisciplinary team. The Respiratory Physiotherapy HERMES project aims to standardise treatment of patients within and beyond Europe The ERS Respiratory Physiotherapy HERMES Task Force would like to acknowledge each of the national experts who took part in the Delphi process and contributed their feedback to develop a consensus-based international syllabus in respiratory physiotherapy. Europe: Austria: Michaela Strauss; Belgium: Veronica Barbier, Michelle Norrenberg; Bulgaria: Blagoi Marinov; Croatia: Snjezana Benko; Czech Republic: Katerina Neumannova; Denmark: Linette Marie Kofod; Estonia: Karin Tammik; Finland: Tiina Kaistila; France: Adrian Morales Robles, Philippe Joud; Germany: Kathrin Suess; Greece: Eirini Grammatopoulou, Eleni Kortianou; Iceland: Harpa Arnardottir; Italy: Sara Mariani, Luciana Ptacinsky, Francesco D'Abrosca; Ireland: Claire Egan; Lithuania: Ieva Jamontaite; Malta: Stephen Montefort; the Netherlands: Sandra Jongenotter, Susanne van Riesen; Norway: Ulla Pedersen; Poland: Teresa Orlik, Roman Nowobilski; Portugal: Miguel Goncalves, Paulo Abreu, Alda Marques; Spain: Jordi Vilaró, M. Angels Cebriá I Iranzo, Rosa Josa; Sweden: Louise Lannefors, Karin Wadell; Switzerland: François Vermeulen; Turkey: Hulya Arikan, Sema Savci; Ukraine: Kateryna Tymruk; UK: Abebaw Yohannes, Bronwen Connolly, Judy Bradley, Brenda O'Neill. Outside Europe: Australia: Jennifer Alison, Lissa Spencer, Shane Patman; Argentina: Gustavo Olguin; Brazil: Sara Menezes, Verônica Parreira; Canada: Darlene Reid, Elisabeth Dean, Didier Saey; Thailand: Chulee Jones; USA: Donna Frownfelter, Thomas Kallstrom. Further acknowledgement should also go to Julia Bott (Academic Health Science Network, UK) who was influential in preparing the initial project proposal.
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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 | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.016 |
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".