Healthcare needs, experiences and treatment burden in primary care patients with multimorbidity: An evaluation of process of care from patients' perspectives
Bibliographic record
Abstract
BACKGROUND: Patients with multimorbidity often experience treatment burden as a result of fragmented, specialist-driven healthcare. The 'family doctor team' is an emerging service model in China to address the increasing need for high-quality routine primary care. OBJECTIVE: This study aimed to explore the extent to which treatment burden was associated with healthcare needs and patients' experiences. METHODS: Multisite surveys were conducted in primary care facilities in Guangdong province, southern China. Interviewer-administered questionnaires were used to collect data from patients (N = 2160) who had ≥2 clinically diagnosed long-term conditions (multimorbidity) and had ≥1 clinical encounter in the past 12 months since enrolment registration with the family doctor team. Patients' experiences and treatment burden were measured using a previously validated Chinese version of the Primary Care Assessment Tool (PCAT) and the Treatment Burden Questionnaire, respectively. RESULTS: The mean age of the patients was 61.4 years, and slightly over half were females. Patients who had a family doctor team as the primary source of care reported significantly higher PCAT scores (mean difference 7.2 points, p < .001) and lower treatment burden scores (mean difference -6.4 points, p < .001) when compared to those who often bypassed primary care. Greater healthcare needs were significantly correlated with increased treatment burden (β-coefficient 1.965, p < .001), whilst better patients' experiences were associated with lower treatment burden (β-coefficient -0.252, p < .001) after adjusting for confounders. CONCLUSION: The inverse association between patients' experiences and treatment burden supports the importance of primary care in managing patients with multimorbidity. PATIENT CONTRIBUTION: Primary care service users were involved in the instrument development and data collection.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".