Effectiveness of Research-Oriented Integrated Nursing Interventions on Cancer Pain Management in Chinese Hospitalized Oncology Patients A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: To systematically evaluate the effectiveness of research-oriented integrated nursing interventions on cancer pain management in hospitalized oncology patients in China. Methods: A computerized search of Chinese and English databases was conducted to identify relevant studies. Two researchers independently assessed the quality of included literature using the Newcastle-Ottawa Scale (NOS). Data were extracted and analyzed via Stata 14. A random-effects model was applied due to significant heterogeneity (I² > 50%). Sensitivity analysis and Egger’s test were performed to assess bias. Results: 12 eligible studies (2014–2024) were included. Meta-analysis demonstrated that integrated nursing interventions significantly reduced cancer pain scores compared to routine care (SMD = -1.51, 95% CI: -1.90 to -1.12; I²= 84.8%), with superior efficacy. Subgroup-analyses revealed enhanced effects for "Nursing modes" (SMD = -2.11) and "cancer pain education" (SMD = -2.30). Conclusion: Research-oriented integrated nursing interventions significantly improve cancer pain management in Chinese hospitalized oncology patients, particularly through synergistic effects of "Nursing modes" and "cancer pain education." However, implementation bias from "additive interventions" in teaching hospitals and high heterogeneity warrant attention. Future studies should optimize designs to enhance clinical applicability.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".