Using shared mental models to conceptualize patients as professionals, decision-makers, collaborators, and members of interprofessional healthcare teams
Bibliographic record
Abstract
Patient engagement has become the buzz-phrase of 21st Century health care. Around the world, healthcare systems involve patients in a wide range of activities including drug development, research, and policy design. There are strong institutional pressures for patient engagement in healthcare activities that have been bolstered by ethical imperatives and social and organizational benefits from patient engagement. There is a trend to center efforts to cultivate engagement initiatives that are meaningful to patients and family. However, these efforts are characterized by multiple challenges, for example, tokenism and the lack of organizational support. These barriers may persist in healthcare professionals’ conceptualizations of patients as independent from the health system; healthcare professionals are active shapers of health services and patients are passive recipients. There is a growing need to address the scholarly confusion with the roles and expectations of patients in healthcare activities, and what strategies can support more meaningful and collaborative relationships between different groups. This paper uses the literature on shared mental models - knowledge structures that define the boundaries of collaboration between groups with distinct values and beliefs - to describe how the roles of patients in healthcare activities may be expanded. This paper deconstructs how technical and informal knowledge serves as a focal point for healthcare professional identity, and how this relationship between knowledge and professionalism creates an anchor for conceptualizing patients as professionals, collaborators, and decision-makers. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.010 |
| 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".