目的旨在对我国护士疼痛管理知识和态度进行分析、评价,以全面了解和评估当前我国护士疼痛管理的研究现状和护理实践中的局限性。方法系统检索PubMed、Embase、Cochrane Library、Web of Science、中国知网、万方数据库、维普中文科技...目的旨在对我国护士疼痛管理知识和态度进行分析、评价,以全面了解和评估当前我国护士疼痛管理的研究现状和护理实践中的局限性。方法系统检索PubMed、Embase、Cochrane Library、Web of Science、中国知网、万方数据库、维普中文科技期刊数据库,收集有关中国护士疼痛管理知识和态度的横断面研究,检索时间为建库至2023年6月。采用了美国卫生保健研究与质量机构(AHRQ)的质量评价标准对最终纳入文献的质量进行了严格评估,并运用Stata 15.1软件进行全面统计分析。结果本研究最终纳入35篇文献,涉及14146名护士。11篇文献为高质量文献,其余24篇文献质量均处于中等水平。结果显示,中国护士疼痛管理知识和态度问卷答对率为46%(95%CI:0.43~0.50)。亚组分析显示,研究生及以上学历、工作后继续接受疼痛教育、在肿瘤科工作的护士疼痛管理知识和态度问卷答对率越高,分别是56.70%(95%CI:43.80%~69.70%)、58.60%(95%CI:55.00%~62.20%)、58.90%(95%CI:45.20%~72.70%)。结论我国护士在疼痛管理领域的知识和态度普遍存在不足,提示护理管理者亟需认识到疼痛管理在整体护理质量中的重要性,有必要制订持续性的专业培训和教育计划,通过提供定期的疼痛管理知识更新,以提升护士对疼痛管理的认知和应对能力,进而提升护士专业素养,为疼痛患者提供更全面、人性化的治疗和关怀,从而改善疼痛患者的生活质量和治疗效果,进而提高整体护理质量。展开更多
A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data....A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.展开更多
文摘目的旨在对我国护士疼痛管理知识和态度进行分析、评价,以全面了解和评估当前我国护士疼痛管理的研究现状和护理实践中的局限性。方法系统检索PubMed、Embase、Cochrane Library、Web of Science、中国知网、万方数据库、维普中文科技期刊数据库,收集有关中国护士疼痛管理知识和态度的横断面研究,检索时间为建库至2023年6月。采用了美国卫生保健研究与质量机构(AHRQ)的质量评价标准对最终纳入文献的质量进行了严格评估,并运用Stata 15.1软件进行全面统计分析。结果本研究最终纳入35篇文献,涉及14146名护士。11篇文献为高质量文献,其余24篇文献质量均处于中等水平。结果显示,中国护士疼痛管理知识和态度问卷答对率为46%(95%CI:0.43~0.50)。亚组分析显示,研究生及以上学历、工作后继续接受疼痛教育、在肿瘤科工作的护士疼痛管理知识和态度问卷答对率越高,分别是56.70%(95%CI:43.80%~69.70%)、58.60%(95%CI:55.00%~62.20%)、58.90%(95%CI:45.20%~72.70%)。结论我国护士在疼痛管理领域的知识和态度普遍存在不足,提示护理管理者亟需认识到疼痛管理在整体护理质量中的重要性,有必要制订持续性的专业培训和教育计划,通过提供定期的疼痛管理知识更新,以提升护士对疼痛管理的认知和应对能力,进而提升护士专业素养,为疼痛患者提供更全面、人性化的治疗和关怀,从而改善疼痛患者的生活质量和治疗效果,进而提高整体护理质量。
基金sponsored by the National Basic Research Program of China (Grant No. 2012CB955202)the China Scholarship Council under the Joint-PhD program for conducting research at CSIROsupported by the Indian Ocean Climate Initiative
文摘A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.
