Exploring provider roles, continuity, and mental models in cirrhosis care: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Advanced cirrhosis results in frequent emergency department visits, hospital admissions and readmissions, and a high risk of premature death. We previously identified and compared 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 in care. The aim of this paper is to further explore how challenges to continuity and coordination of care influence how health care providers adapt in their approaches to and development of mental models of cirrhosis care. METHODS: Cross-sectional formal elicitation of mental models using Cognitive Task Analysis. Purposive and chain-referral sampling took place over 6 months across Alberta for a total of 19 participants, made up of family physicians ( n = 8), specialists ( n = 9), and cirrhosis nurse practitioners ( n = 2). RESULTS: Lack of continuity in cirrhosis care, particularly informational and management continuity, not only hinders health care providers’ ability to develop rich mental models of cirrhosis care but may also determine whether they form a patient-centred or task-based mental model, and whether they develop shared mental models with other providers. CONCLUSIONS: The system barriers and gaps that prevent the level of continuity needed to coordinate care for people with cirrhosis lead providers to create and work under mental models that perpetuate those barriers, in a vicious cycle. Understanding how providers approach cirrhosis care, adapt to the challenges facing them, and develop mental models offers insights into how to break that cycle and improve continuity and coordination.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".