Multimorbidity, service organization and clinical decision making in primary care: a qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Primary care professionals often manage patients with multiple long-term health conditions, but managing multimorbidity is challenging given time and resource constraints and interactions between conditions. OBJECTIVE: To explore GP and nurse perceptions of multimorbidity and the influence on service organization and clinical decision making. METHODS: A qualitative interview study with primary care professionals in practices in Greater Manchester, U.K. Interviews were conducted with 15 GPs and 10 practice nurses. RESULTS: Primary care professionals identified tensions between delivering care to meet quality targets and fulfilling the patient's agenda, tensions which are exacerbated in multimorbidity. They were aware of the inconvenience suffered by patients through attendance at multiple clinic appointments when care was structured around individual conditions. They reported difficulties managing patients with multimorbidity in limited consultation time, which led to adoption of an 'additive-sequential' decision-making model which dealt with problems in priority order until consultation resources were exhausted, when further management was deferred. Other challenges included the need for patients to co-ordinate their care, the difficulties of self-management support in multimorbidity and problems of making sense of the relationships between physical and mental health. Doctor and nurse accounts included limited consideration of multimorbidity in terms of the interactions between conditions or synergies between management of different conditions. CONCLUSIONS: Primary care professionals identify a number of challenges in care for multimorbidity and adopt a particular model of decision making to deliver care for multiple individual conditions. However, they did not describe specific decision making around managing multimorbidity per se.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it