Primary care physicians’ work conditions and their confidence in managing multimorbidity: a quantitative analysis using Job Demands–Resources Model
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is a global issue that presents complex challenges for physicians, patients, and health systems. However, there is a lack of research on the factors that influence physicians' confidence in managing multimorbidity within primary care settings, particularly regarding physicians' work conditions. OBJECTIVES: Drawing on the Job Demands-Resources Model, this study aims to investigate the level of confidence among Chinese primary care physicians in managing multimorbidity and examine the predictors related to their confidence. METHODS: Data were collected from 224 physicians working in 38 Community Healthcare Centres (CHCs) in Shanghai, Shenzhen, Tianjin, and Jinan, China. Work-family conflict (WFC) perceived organizational support (POS), self-directed learning (SDL), and burnout were measured. Physicians' confidence was assessed using a single item. Mediation effect analysis was conducted using the Baron and Kenny method. RESULTS: The results showed that the mean confidence score for physicians managing multimorbidity was 3.63 out of 5, only 20.10% rating their confidence level as 5. WFC negatively related physicians' confidence and POS positively related physicians' confidence in multimorbid diagnosis and treatment. Burnout fully mediated the relationship between WFC and physicians' confidence, and SDL partially mediated the relationship between POS and physicians' confidence. CONCLUSIONS: The confidence level of Chinese primary care physicians in managing multimorbidity needs improvement. To enhance physicians' confidence in managing multimorbid patients, CHCs in China should address WFC and burnout and promote POS and SDL.
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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 | 0.001 | 0.002 |
| 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 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".