019 Training practitioners into interprofessional shared decision-making: lessons learned from a pilot pedagogical sequence
Bibliographic record
Abstract
Introduction Patient engagement is acknowledged to serve as a key driver in transforming the healthcare system. This applies across the spectrum of care, health policies, research, and education. In Switzerland, patient expertise is mobilised in a postgraduate curriculum, the Certificate of Advanced Studies (CAS) in Care Coordination and Networking,1 following the different levels of engagement described in the Montreal Model.2 This includes high fidelity shared decision-making simulation with patients, conferences given by patient-as-partners and co-teaching with professionals. This CAS is attended by experienced professionals in nursing, physiotherapy, nutrition, or social work. The competences targeted include the ability to partner with professionals and people involved, and to master shared decision-making methods and tools.3–6 For this purpose, we built a pilot pedagogical sequence to provide an opportunity for practitioners to experience, observe and reflect upon shared decision-making process and partnership. Methods The aim of our research was to explore a) the feasibility and acceptability of this pedagogical sequence, b) its preliminary benefits for professionals, and c) the implications of a collaboration with a patient-as-partner. Mixed methods were used for this exploratory study conducted in 2023. Data collection included questionnaires and individual essays assessing attitudes towards shared decision- making and video recordings of the simulation. Results Findings show that a) the sequence was delivered as intended; b) professionals engaged in the sequence, identified transfer opportunities, and implemented some of them into their practice; and c) the engagement of the patient-as-partner allowed the professionals to change their attitudes towards shared decision-making. Discussion and Conclusion This research will help adjust the CAS’s next edition. More broadly, it will contribute to shared decision-making training, and to the implementation of the patient-as-partner model in professional training. References HES La Source. Formation continue: CAS Coordination des soins et travail en réseau. Institut et Haute Ecole de la Santé La Source. Published June 2023. www.ecolelasource.ch/formations/postgrade/cas/coordination-soins-reseau/ Pomey MP, Flora L, Karazivan P,….Jouet E. Le ‘Montreal model’: enjeux du partenariat relationnel entre patients et professionnels de la santé [The Montreal model: the challenges of a partnership relationship between patients and healthcare professionals]. Santé Publique. 2015;1(HS):41–50. doi:doi.org/10.3917/spub.150.0041 Dawn S, Légaré F. Adopter une approche interprofessionnelle de prise de décision partagée pour encourager l’implication des patients [Engaging patients using an interprofessional approach to shared decision making]. Can Oncol Nurs J. 2015;25(4):455–469. Thériault G, Bell NR, Grad R, Singh H, Szafran O. Enseigner la prise de décision partagée. Le Médecin de famille canadien. 2019;65:e312–e324. Dogba MJ, Menear M, Stacey D, Brière N, Légaré F. The evolution of an interprofessional shared decision-making research program: reflective case study of an emerging paradigm. IntJIntegrCare.2016;16(3).doi:10.5334/ijic.2212. Lewis KB, Stacey D, Squires JE, Carroll S. Shared decision-making models acknowledging an interprofessional approach: a theory analysis to inform nursing practice. Res Theory Nurs Pract. 2016;30(1):26–43.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".