The organization-level and physician-level factors associated with primary care physicians’ confidence in pandemic response: A multilevel study in China
Bibliographic record
Abstract
Primary care physicians (PCPs) suffered from heavy workloads and health problems during COVID-19 pandemics, and building their confidence in pandemic response has great potential to improve their well-being and work performance. We identified the organizational factors associated with their confidence in pandemic response and proposed potential management levers to guide primary care response for the pandemic. We conducted a cross-sectional survey with 224 PCPs working in 38 community health centers in China. Guided by self-efficacy theory, organization-level factors (organizational structure and organizational culture) and physician-level factors (job skill variety, perceived organizational support, work-family conflict, and professional fulfillment) were selected, and two-level ordinal logit models were built to examine their association with PCPs' confidence in pandemic response. We found that hierarchical culture (OR = 3.51, P<0.05), perceived organizational support (OR = 2.36, P<0.05), job skill variety (OR = 1.86, P<0.05), and professional fulfillment (OR = 2.26, P<0.05) were positively associated with PCPs' confidence in pandemic response. However, the influence of organization structure and work-family conflict seemed limited. The study not only increases our understanding of the influence of organizational context on PCPs' pandemic response confidence, but also points out potential management levers for front-line primary care managers to enhance primary care pandemic response capacity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".