Family Physicians’ Mental Models of Symptom Management in Cirrhosis Care
Bibliographic record
Abstract
Context: Our previous research found a lack of role clarity, and differences in how primary and specialty care managed cirrhosis care. In this study we explored more deeply, specifically around symptom management. Objective: To make recommendations for a province-wide cirrhosis management program based on how family physicians conceptualize cirrhosis symptom management, their role in it, and whether or how they incorporate palliative principles into their approach. Study Design and Analysis: Cross-sectional Cognitive Task Analysis study, using our previously published framework-guided qualitative analysis. Setting: Private community practices in Alberta, Canada. Population Studied: Family physicians who saw small numbers (typical for unspecialized practice) of cirrhosis patients. 4 were women, median age 47, median years in practice 16, none in rural practice. Intervention/Instrument: Knowledge Audit method Cognitive Task Analysis interviews. Outcome Measures: Detailed description of mental models of symptom management in cirrhosis care. Recommendations based on findings. Results: Family physicians develop reactive mental models for symptom management, using a case-by-case approach focusing on the most important symptoms or what matters most to the patient, rather than generalized or guideline-based models. Reactive mental models are linked to the lack of formal structure, guidance, and clarity of roles in cirrhosis care, as well as physicians’ need for knowledge on demand (information physicians can access at the place and time of need) for each patient. Family physicians regarded palliative care as part of their responsibility but did not have clear models of when and how to have these conversations. As a result, palliative principles were not a clearly integrated component of their mental models of cirrhosis care. Conclusions: Improving symptom management in cirrhosis care requires clearly defined roles and responsibilities for all health team members. Creating programs like those for other chronic illnesses (e.g., diabetes, heart failure) to provide the knowledge on demand and operational guidance family physicians was recommended and will be implemented. Tools and supports that integrate palliative care and provide direction for family physicians on when and how to have conversations with patients throughout the trajectory of the illness will be developed.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".