Eliciting and Understanding Primary Care and Specialist Mental Models of Cirrhosis Care: A Cognitive Task Analysis Study
Bibliographic record
Abstract
Background: Gaps in coordination and transitions of care for liver cirrhosis contribute to high rates of hospital readmissions and inadequate quality of care. Understanding the differences in the mental models held by specialty and primary care physicians may help to identify the root causes of problems in the coordination of cirrhosis care. Aim: To compare and identify differences in the mental models of cirrhosis care held by primary and specialty care physicians and nurse practitioners that may be addressed to improve coordination and transitions. Methods: = 2) across Alberta. Results: Family physicians do not maintain rich mental models of cirrhosis care. They see cirrhosis patients relatively infrequently, rebuilding their mental models when required (knowledge on demand). They have reactive and patient-need-focused, rather than proactive and system-of-care, mental models. Specialists' mental models are rich but vary widely between patient-centered and task-centered and in the degree to which they incorporate responsibility for addressing system gaps. Nurse practitioners hold patient-centered mental models like specialists but take responsibility for addressing gaps in the system. Conclusions: Improving the coordination of cirrhosis care will require infrastructure to design care pathways and work processes that will support family physicians' knowledge-on-demand needs, facilitate primary care-specialist relationships, and deliberately work toward building a shared mental model of responsibilities for addressing medical care and social determinants of health.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".