Bespoke to the patient: a qualitative study on learning to manage multimorbidity in family medicine
Bibliographic record
Abstract
Introduction: The rising prevalence of multimorbidity poses a significant challenge to healthcare systems. However, medical education predominantly emphasizes single-disease frameworks, offering limited guidance on how learners can navigate the complexities of managing co-existing health conditions. Given the high incidence of multimorbidity in family medicine, this study aimed to explore the experiences of family medicine residents in managing multimorbidity, with the goal of informing curriculum development. Methods: We conducted a qualitative study comprising four focus groups (mean duration 47 minutes) with a convenience sample of 28 family medicine residents learning in urban and rural settings. Data were analyzed inductively using reflexive thematic analysis. We drew on generalism and adaptive expertise as sensitizing theoretical lenses to support thematic development and our final interpretation. Results: Participants described a shift from their undergraduate focus on "getting the list" of diagnoses toward a more nuanced, patient-centred approach to multimorbidity, which they characterized as "bespoke to the patient." Throughout residency, learners reported increased confidence conducting more flexible consultations-incorporating social determinants of health, the unique patient's context, realizing and navigating how healthcare structures impact, and sometimes impede, patient care. Balancing competing priorities became a key feature of their evolving practice, supported by exposure to diverse patient populations, meaningful preceptor relationships, and varied clinical environments. Residents increasingly identified their role coordinating the patient's care team, leveraging a generalist perspective to organize care and address complexity. Conclusions: Family medicine residents described learning to manage multimorbidity as a developmental process of acquiring generalist adaptive expertise, supported through working in a variety of learning environments. Encouraging preceptors to explicitly share strategies-such as managing limited time and navigating health system constraints-may further enhance resident education in caring for patients with multimorbidity.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".