The Intention of Primary Health Nurses to Participate in Internet Plus Nursing Service: Cross-Sectional Survey
Bibliographic record
Abstract
Background: "Internet Plus Nursing Service" (IPNS) offers innovative solutions for China's growing home health care demands. Understanding primary care nurses' participation intentions is crucial for service optimization. Objective: This study evaluates primary health nurses' intention to participate in IPNS-a technology-mediated home care model combining mobile health platforms with in-person visits-and examines how digital readiness, safety perceptions, and organizational factors influence participation decisions, to guide policy optimization for scalable digital home health care delivery. Methods: A cross-sectional survey was conducted in Jiangsu Province, China (December 2023-December 2024) using the validated Participation Intention of Nurses on IPNS Scale. Convenience sampling enrolled 3952 nurses from 13 prefecture-level cities in Jiangsu-the second-tier administrative divisions in China that typically encompass both urban and rural areas, each with independent health care systems governed by municipal health authorities. Statistical analyses included t tests and ANOVA with SPSS 22. Results: A total of 3952 surveys were completed. The participation intention scale yielded a mean (SD) total score of 66.13 (7.89) across respondents. Subscale analysis revealed mean (SD) scores of 18.57 (2.68) for participation attitude, 18.87 (2.49) for subjective norms, and 25.67 (3.48) for perceived behavioral control. Significant demographic predictors of participation intention were identified through statistical analysis. Male nurses demonstrated stronger intention (t72.974=-23.139, P<.0001), as did those over 30 years old (F39,51=27.215, P<.0001) and bachelor's degree holders (t2185.018=-4.994, P<.0001). Workplace characteristics also showed significant associations, with nursing management department staff (F39,51=45.877, P<.0001) and those with less organizational workloads (F39,51=9.829, P<.0001) displaying greater intention. Professional factors including higher positional rank (F39,51=37.32, P<.0001), more advanced titles (F39,51=30.176, P<.0001), and over 11 years of experience (F39,51=5.242, P=.001) predicted stronger participation intent. Finally, nurses earning 5000-10,000 RMB (a currency exchange rate of RMB 1=US $0.71 is applicable) monthly showed significantly higher intention scores (F39,51=16.141, P<.0001). Conclusions: Policymakers should prioritize 3 interventions: (1) develop IPNS-specific safety protocols and legal safeguards, (2) optimize workload allocation through intelligent scheduling systems, and (3) establish tiered incentive mechanisms targeting middle-income nurses and experienced practitioners.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".